Implementação de um sistema de iluminação dianteiro adaptativo a

Implementação de um sistema de iluminação dianteiro
adaptativo a LEDs para caminhões pesados
dimensionado para o mercado Brasileiro
Luciano P. Lukacs
TESE SUBMETIDA AO CORPO DOCENTE DA COORDENAÇÃO DO
PROGRAMA DE PÓS-GRADUAÇÃO DE ENGENHARIA INDUSTRIAL DA
UNIVERSIDADE FEDERAL DA BAHIA COMO PARTE DOS REQUISITOS
NECESSÁRIOS PARA A OBTENÇÃO DO GRAU DE DOUTOR EM CIÊNCIAS
EM ENGENHARIA INDUSTRIAL.
Aprovada por:
________________________________________________
Prof. Marcelo Embiruçu, D.Sc.
________________________________________________
Prof. Iuri Muniz Pepe, D.Sc.
________________________________________________
Prof. Ednildo Andrade Torres, D.Sc.
________________________________________________
Prof. Carlos Arthur Teixeira Cavalcante, D.Sc.
________________________________________________
Prof. José Maria Campos Dos Santos, D.Sc.
________________________________________________
Prof. Paulo César Machado de Abreu Farias, D.Sc.
________________________________________________
Celso Duarte, PhD
SALVADOR, BA - BRASIL
DEZEMBRO DE 2009
LUKACS, LUCIANO
Implementação de um sistema de
iluminação dianteiro adaptativo a LEDs
para
caminhões
dimensionado
para
pesados
o
mercado
Brasileiro [Salvador] 2009.
XII, 196 p. 29,7 cm (PEI/UFBA,
D.Sc., Engenharia Industrial, 2009).
Tese - Universidade Federal da
Bahia, PEI
1. Fuzzy, 2. LED, 3. AFS, 4. Controle
I.
UFBA.
Título
(série)
Agradecimentos
À minha família por tudo, especialmente nos momentos mais difíceis
Aos meus orientadores, Iuri e Marcelo por terem não só me orientado, mas
também me agüentado neste período
À CAPES, em especial ao Programa PROCAD, pelo suporte ao
desenvolvimento do trabalho
Ao Mahendra Dassanayake, pelo apoio ao longo de todo processo e de
sempre estar disposto a discutir novas idéias
À Ford pelo apoio na aquisição de dados e informações
À UFBA/LAPO pelo apoio recebido, oportunidades proporcionadas e
ensinamentos passados
Aos colegas do LAPO pelo apoio recebido, em especial ao Luiz, João, Itã,
Valmara, Cafu, Vitor, Kelly e Fróes
A todos aqueles que contribuíram de uma forma ou de outra para a elaboração
deste trabalho, em especial aos colegas, alunos e professores
Ao meu irmão Leonardo, pela ajuda nos momentos de maior tensão
Ao afilhado, o pequeno Luciano, pela alegria que me trás todo dia
A Raquel e Tiffany por sempre me apoiarem e insistirem que eu deveria ir até o
fim
A Aline, por tudo que passamos juntos
iii
Dedicatória
To Grandpa, needless to say, I miss you everyday…
iv
Índice
Capítulo 1. Introdução
1
Opening Statement
1
Apresentação
2
1.1. Os objetivos e a importância do estudo e do desenvolvimento de
4
um sistema auxiliar de iluminação automotiva
1.2. As Etapas e a organização do trabalho realizado
9
Nomenclatura
12
Referências
13
Capítulo 2. Estudo comparativo entre os LEDs: Golden Dragon
14
(Osram), K1 e K2 (Lumileds)
Apresentação
14
Resumo
14
Conclusão
15
Opening statement
17
Abstract
17
2.1. Introduction
19
2.2. Experimental study
21
2.3. Method and materials
22
2.4. Results
24
2.5. Conclusion
32
References
33
Capítulo 3. Benefícios e desafios de controlar um AFS (Adaptive
34
Front-lighting System, sistema adaptativo de
iluminação veicular) a LEDs adotando a lógica Fuzzy
Apresentação
34
Resumo
36
Conclusão
37
Opening statement
39
Abstract
40
v
3.1. Introduction
42
3.2. Fuzzy knowledge base and membership functions
46
3.3. The lighting device and hardware proposed
50
3.4. Methodology
54
3.5. Results and discussion
61
3.6. Conclusion
67
Nomenclature
68
References
69
Capítulo 4. Desenvolvimento de um modelo AFS (sistema
75
adaptativo de iluminação veicular) para assegurar as
necessidades do motorista de caminhão durante o
período noturno e vislumbrando sua utilização para o
mercado Brasileiro
Apresentação
75
Resumo
76
Conclusão
77
Opening statement
79
Abstract
80
4.1. Introduction
81
4.2. Road conditions and environment
82
4.3. Data from the driver survey
88
4.4. Test track measurements
94
4.5. Modeling and results
95
4.6. Conclusion
106
Nomenclature
107
References
107
Capítulo 5. Desenvolvimento do protótipo do AFS baseado em
111
LEDs
Apresentação
111
Resumo
111
Conclusão
112
Opening statement
114
vi
Abstract
114
5.1. Introduction
115
5.2. Looking for a product
115
5.2.1 LED
116
5.2.2 Optics
120
5.2.3 The selected LED
121
5.2.4 Heat sinking
122
5.2.5 The power electronics
123
5.2.5.1 Connectors
126
5.2.6 Microcontroller
129
5.2.7 The controlling software
130
5.2.8 The sensor definition
131
5.3. Experimental
5.3.1 Test setup
132
134
5.4. Appearance
135
5.5. System integration and application
136
5.5.1 Sensor definition
137
5.5.2 Control strategy and benefits
137
5.6. Simulation and experimental comparison
138
5.7. Conclusion
145
Nomenclature
146
References
146
Capítulo 6. Conclusões e sugestões para trabalhos futuros
147
Opening statement
148
Apresentação
148
6.1. Comentários finais
149
6.2. Sugestões para trabalhos futuros
153
Apêndice A. Anteprojeto do relatório descritivo da patente de
155
invenção: farol auxiliar e processos relacionados
Apêndice B. Código computacional no MATLAB
163
vii
Apêndice C. Código computacional do protótipo
172
Apêndice D. Carta de intenção do desenvolvimento do produto
190
vii
Resumo da Tese apresentada ao PEI/UFBA como parte dos requisitos
necessários para a obtenção do grau de Doutor em Ciências (D.Sc.)
Implementação de um sistema de iluminação dianteiro
adaptativo a LEDs para caminhões pesados
dimensionado para o mercado Brasileiro
Luciano P. Lukacs
Dezembro/2009
Orientadores:
Iuri Muniz Pepe
Marcelo Embiruçu
Programa:
Engenharia Industrial
Recentemente, o sistema de iluminação veicular tem tido grandes
avanços tecnológicos tais como a utilização de um sistema dianteiro adaptativo
de iluminação (AFS), o qual representa um avanço industrial de tecnologia
referente a iluminação. Este trabalho apresenta uma solução viável técnica e
economicamente, desenvolvida especificamente para o mercado Brasileiro,
para implementar esta tecnologia em caminhões pesados. O trabalho também
discute o impacto e os benefícios para o consumidor, sendo que este sistema
tem por principio adicionar luz, fazendo com que a direção noturna se torne
mais segura. Três pontos são levados em consideração e analisados: o
produto, a integração dos seus componentes e a aplicação do sistema e sua
aceitação pelo critério do motorista.
ix
Abstract of Thesis presented to PEI/UFBA as a partial fulfillment of the
requirements for the degree of Doctor of Science (D.Sc.)
Implementation an AFS led system on heavy trucks
tailored for the Brazilian market
Luciano P. Lukacs
December/2009
Advisors:
Iuri Muniz Pepe
Marcelo Embiruçu
Programme:
Industrial Engineering
Recently, the vehicular illumination system has had huge technological
advances such as the use of an adaptive front-lighting system (AFS), which
represents an industry breakthrough in lighting technology. This work presents
a feasible and affordable solution, tailored for the Brazilian market, to
implement this system on heavy trucks. It also discusses the impact and
benefits for the costumer, since the main goals is to add light, making night time
driving safer. Three key aspects are analyzed and taken into account: product,
system integration and application, and customer acceptance criteria.
x
Lista de publicações
Apresentações em congressos e conferências
•
Lukacs, L. Desenvolvimento de um sistema auxiliar de iluminação
automotiva a LEDs servo assistida, VIII Seminário de Pesquisa e PósGraduação (VIII SEMPPG), 08 de novembro, 2007.
•
Lukacs, L. Impact of adopting LED Advanced Font Lighting Systems
(AFS) on Heavy Trucks in the South American Region, 3rd Annual
Automotive Lighting Design & Technologies conference, Frankfurt,
Germany, Jan 28- 30, 2008.
•
Lukacs, L. Explore Benefits and Implications of LED Advanced Font
Lighting Systems (AFS) on Heavy Trucks in the South American Region,
th
4 Annual Automotive Lighting & Design Conference, The Dearborn Inn
in Dearborn, USA, April 21 - 23, 2008.
Trabalhos completos em congressos
•
Lukacs, L.; Reis, M; Silva, K; Pepe, I.; Embirucu, M. Comparison among
white high power LEDs, CONEM, 2008.
•
Lukacs, L., Magalhães, R, Pepe, I., Embiruçu, M., Fontes, C.
Implications and outcomes of controlling a LED AFS using Fuzzy logic
for the South American Markets, 8th International Lighting Symposium,
2009.
Artigos completos em periódicos indexados
•
Lukacs, L., Simões, L., Pepe, I., Embiruçu, M. Impact of adopting Fuzzy
logic to control an LED AFS (adaptive front-lighting system), SAE
Technical Paper Series, No. 2007-01-2784, 2007.
•
Lukacs, L., Rodriguez, P. A., Pepe, I., Simões, L., Embiruçu, M. Benefits
and Implications of the LED AFS on Heavy Trucks in the South
American Region, SAE Technical Paper Series, No. 2008-36-0014,
2008.
•
Lukacs, L., Pepe, I., Embiruçu, M., Fontes, C. Implications and outcomes
of controlling a LED AFS (Adaptive Front-Lighting System) using fuzzy
xi
logic for the South American Markets, SAE Technical Paper Series, No.
2009-36-0027, 2009.
•
Lukacs, L., Magalhães, R., Dassanayake, M., Fontes, C., Pepe, I.,
Embiruçu, M. Benefits and Challenges of Controlling a LED AFS
(Adaptive Front-lighting System) using Fuzzy Logic, The International
Journal of Automotive Technology, submitted, 2009.
•
Lukacs, L., Magalhães, R., Dassanayake, M., Fontes, C., Pepe, I.,
Embiruçu, M. Design of a fuzzy-based AFS (Advanced Front Lighting
System) model to meet truck driver's needs during nighttime driving:
foreseeing its use in the Brazilian market. Journal of Intelligent
Transportation
Systems:
Technology,
Planning,
and
Operations,
submitted, 2009.
xii
Chapter 1
Introduction
Opening statement
This study embraces a significant technological contribution to the
automotive industry. This has been accomplished through the development of
specific techniques regarding the use of a Light Emitting Diode (LED) Adaptive
Front Lighting System (AFS) on heavy trucks, especially in the South American
market and focusing in Brazil, without masking their core scientific content.
Also, a feasible proposal for an efficient vehicle light intensity automatic control
has been well defined and successful to implement. It is important to highlight
that the truck driver in Brazil needs a better light distribution to make the
nighttime driving safer due mainly to the road conditions and environment. The
LED AFS system is an enhancement regarding safety due to its functionality
and to the fact that it incorporates more light to the driver beam pattern.
The following chapters are arranged in a systemic sequence displaying
the methodological development adopted. The results achieved in each phase
of this study are shown highlighting their individual significance to characterize
the problems that were discussed and to validate the techniques and methods
which led to the evolution of the final technological product.
Keywords: LED, fuzzy, control, AFS
1
Capítulo 1
Introdução
Apresentação
Este trabalho possui notadamente uma significativa contribuição
tecnológica, sem prejuízos para o seu conteúdo científico, também presente de
forma importante ao longo das aplicações e técnicas desenvolvidas. O impacto
tecnológico
desta
tese
de
doutoramento reside,
sobretudo,
em
sua
singularidade, o que comprovadamente pode ser constatado, mediante
pesquisa na literatura aberta, pela inexistência de desenvolvimentos ou
avanços relacionados à aplicação de um sistema de AFS com LEDs para
caminhões no mercado da América do Sul (mais especificamente, no Brasil), e
também pela proposta, aplicada e bem sucedida, de uma estratégia eficiente
de controle automático para a intensidade de iluminação veicular. É importante
ressaltar que o motorista de caminhão no Brasil necessita de uma melhor
distribuição de luz a fim de tornar a direção noturna mais segura, devido
principalmente às condições das pistas e do ambiente. O sistema de AFS com
LEDs é um reforço em relação à segurança devido à sua funcionalidade e
principalmente devido ao fato que ele incorpora mais luminosidade ao feixe de
luz do farol.
Os capítulos que seguem mostram de forma sequenciada e sistemática
o desenvolvimento metodológico adotado e os resultados obtidos em cada
etapa do trabalho, ressaltando-se a importância de cada uma destas fases para
2
a caracterização dos problemas envolvidos, aplicação e validação das técnicas
e metodologias propostas, e evolução no sentido do produto tecnológico final.
Palavras-chave: LED, fuzzy, controle, AFS
3
1.1 Os objetivos e a importância do estudo e do
desenvolvimento de um sistema auxiliar de iluminação
automotiva
Esta tese de doutoramento tem por objetivo o desenvolvimento de um
sistema auxiliar de iluminação automotiva para caminhões pesados usando
LED (Light-Emitting-Diodes, diodo emissor de luz) como fonte de luz branca,
controlado por um sistema decisório com inteligência artificial embarcada,
baseada em diferentes estruturas alternativas da lógica fuzzy. O sistema
proposto possui como meta fundamental aumentar a quantidade de luz nas
laterais e cercanias do veículo, proporcionando maior segurança para o seu
condutor, em especial nas tomadas de curva. O sistema oferece ainda
segurança ativa, redução de energia através do uso de LEDs, controle
automático de intensidade de iluminação e liderança em design. Para tanto,
foram desenvolvidos protótipos de bancada e uma proposta veicular para
validar e testar este conceito. O conceito busca expandir as alternativas de
fontes de energia e principalmente o benefício de adicionar iluminação ao facho
principal do farol dianteiro, gerando incremento gradual de luz ao motorista,
dependendo das condições das estradas e do meio ambiente durante o
período noturno. Os resultados obtidos através do estudo buscaram também a
validação da simulação através do protótipo, bem como a validação do
conceito exposto na proposta. Como análise adicional, foi feito um comparativo
entre os tipos de LEDs a fim de aperfeiçoar o ganho de iluminação e,
consequentemente, o incremento da segurança veicular.
Segundo Neunamm (2006), o desempenho dos sistemas de iluminação
tem tido um grande avanço tecnológico com destaque para o AFS (Adaptive
Front lighting System, sistema dianteiro adaptativo de iluminação) que é uma
4
das tendências atuais do mercado automotivo. Neste contexto, reside um dos
pilares do pioneirismo deste trabalho (Lukacs, 2006, 2007) e de sua
contribuição tecnológica tornando-o, inclusive, passível de depósito e
concessão de propriedade industrial.
A concessão de propriedade industrial está fundamentada no fato de
que o atual estado da técnica não possui uma solução ou uma alternativa para
a adição gradual de iluminação de forma a aumentar o campo de visão do
motorista sem gerar ofuscamento ao tráfego de veículos no sentido contrário.
Atualmente existem no mercado sistemas que possuem um motor elétrico para
acionar o refletor ou modulo elíptico, se por ventura o condutor encontrar um
pedestre. Na maioria das vezes, este pedestre estará no acostamento ou nas
laterais das estradas, sendo de suma importância que possa ser visto e
identificado, para evitar um atropelamento. A Figura 1.1 mostra a diferença na
iluminação de uma pessoa na beira de uma estrada no campo de provas do
fornecedor “Hella” na Alemanha, em uma curva, com e sem o AFS.
Figura 1.1: Comparativo entre iluminação nas laterais sem e com o AFS.
Atualmente, existem três famílias de faróis na indústria automobilística:
•
Lâmpadas com filamento: são as mais comuns devido ao preço
relativamente baixo, sendo a maioria do tipo halogênio;
5
•
Lâmpadas de xenônio: requerem eletrônica adicional de alto custo;
•
LEDs: ainda precisam atingir uma luminância mais alta, suficiente para
serem aplicados em faróis.
A Figura 1.2, a seguir, mostra faróis que utilizam iluminação por meio de
LEDs.
Figura 1.2: Exemplos de faróis que utilizam LEDs.
Um resumo comparando estes três tipos de faróis é mostrado na Tabela
1, na qual os valores de fluxo luminoso são dados em lumens (lm) e a máxima
luminosidade é expressa em milicandelas por metro quadrado (mcd/m2).
Observa-se que há um aumento significativo no fluxo luminoso para as fontes
de luz com LEDs, porém os valores ainda encontram-se abaixo daqueles das
lâmpadas de halogênio usualmente utilizadas.
6
Tabela 1.1: Comparação das características de diferentes tipos de faróis
Máxima
Fluxo
Eficiência Geometria
Fonte de luz
luminosidade
luminoso
[lm/W]
[mm]
[lm]
[mcd/m2]
~ 1.4 x 4.1
Lâmpada de Halogênio H7
1100
26
~ 30
(cilindro)
~ 1.2 x 4.2
Lâmpada de xenônio HID D2S
3200
~ 90
(toróide)
65 (2004)
Diodo Emissor de Luz (LED)
91
~ 27 (2004)
~ 12 (2004)
~ 1.2 x 1.2
80 (2005)
~ 33 (2005)
~ 15 (2005)
(quadrado)
122 (2006)
~ 51 (2006)
~ 13 (2006)
Nota: Se uma fonte luminosa emite uma candela de intensidade luminosa num ângulo sólido de
1 esferorradiano, o fluxo luminoso total emitido dentro daquele ângulo sólido é de um lúmen
(lm). Alternativamente, uma fonte luminosa isotrópica de uma candela emite um fluxo luminoso
total de exatamente 4π lumens. Assim, por exemplo, uma lâmpada de halogênio emite em
todas as direções um total de 1100 lm.
Com o contínuo desenvolvimento tecnológico dos LEDs, existe uma
tendência para um maior uso destes componentes nos sistemas de iluminação
automotiva. Estima-se que nos próximos anos sejam obtidos fluxos luminosos
acima das atuais lâmpadas de halogênio. A Figura 1.3 mostra esta tendência
de desenvolvimento segundo um dos principais fornecedores de LEDs da
indústria atual, a “Lumileds”.
7
Figura 1.3: Previsão de desempenho de iluminação de LED segundo a
Lumileds .
De forma geral, este trabalho compreende o desenvolvimento de um
sistema auxiliar de iluminação automotiva a LED, adotando um modelo
baseado em lógica fuzzy como estratégia de controle da intensidade de
iluminação. Este modelo atua na tomada de decisão de energização da
iluminação auxiliar, resultando na assistência automática ao motorista para
conduzir o veículo em trechos de mudança de direção do feixe de luz dos faróis
dianteiros, considerando-se também a velocidade empregada. A Figura 1.4
demonstra
este
ganho
de
iluminação
progressivo
e
gradual,
cujo
posicionamento dinâmico não requer a necessidade de um motor, ou conjunto
de motores, para deslocar o facho de luz proveniente do farol. Esta figura é
relativa a um farol atual de produção da Ford, do fornecedor “Automotive
Lighting”.
8
Ganho adicional de iluminação
Figura 1.4: Farol baixo na estrada com o incremento do AFS.
Sendo que o objetivo principal é a aplicação do produto em caminhões,
um modelo baseado em lógica fuzzy para caminhões, e também um modelo
para carros foram desenvolvidos (Lukacs et al, 2007). Neste sentido, a
realização de um estudo comparando os hábitos dos motoristas de carro e de
caminhão foi decisiva na construção do modelo para caminhões, cuja
heurística, portanto, tomou como base o comportamento e as características do
consumidor brasileiro.
O sistema desenvolvido atende à norma brasileira do CONTRAN 294/08
(Denatran, 2008), que é similar à norma europeia ECE 48 (UNECE, 2008). Esta
norma é utilizada principalmente nos mercados Sul-Americano, Europeu e
Asiático.
1.2 As Etapas e a organização do trabalho realizado
Este trabalho encontra-se dividido em 6 capítulos organizados de tal
forma que os assuntos são apresentados com um aprofundamento gradativo
no tema. Por outro lado, os capítulos são auto-consistentes, contendo os seus
próprios resumos, objetivos específicos e referências bibliográficas, podendo,
9
desta forma, serem lidos em separado. Não obstante, a fim de reforçar a
continuidade do texto como um todo, um item de contextualização
(apresentação) foi acrescido em cada capítulo, com o objetivo de ressaltar a
inserção de cada capítulo no contexto da tese. Em consonância com a prática
de uso intensivo e difundido da língua inglesa como meio de comunicação e
divulgação, usual na comunidade científica das áreas de engenharia e
tecnologia, e respeitadas as normas do PEI-UFBA, os capítulos que
apresentam os principais resultados e contribuições novos são apresentados
em língua inglesa, a fim de que se possa permitir um maior impacto e um
acesso mais amplo à comunidade científica internacional. Por outro lado, a fim
de garantir um entendimento mínimo em língua portuguesa, todos os itens
chave de cada capítulo, quais sejam, apresentação, resumo e conclusões, são
também exibidos em língua portuguesa. Procedimento análogo, em relação à
língua inglesa, foi adotado nos capítulos cujos textos principais estão escritos
em língua portuguesa. Além disso, também buscando aderência com as
normas científicas internacionais, é adotada a convenção de utilização do
ponto como separador decimal, ao invés da vírgula. Cada capítulo descreve e
representa uma etapa do trabalho, seja esta mais voltada ao desenvolvimento
e aplicação de uma proposta ou simplesmente à consolidação preliminar de
conceitos e conclusões, tendo-se necessariamente o cuidado de destacar a
devida integração entre todas estas etapas.
O capítulo 2 apresenta a definição do tipo de LED a ser escolhido para a
aplicação desejada, com base na análise de suas características. Esta análise
crítica foi importante para o entendimento do potencial da tecnologia que está
sendo adotada e das vantagens e desvantagens dos produtos disponíveis no
10
mercado durante a análise. Vale salientar que o mercado dos LEDs está em
crescimento constante, inclusive com o desenvolvimento de novos produtos.
O capítulo 3 exemplifica, como um primeiro desenvolvimento, a
utilização da lógica fuzzy para controlar o AFS. Esta etapa foi importante para a
constatação do potencial de aplicação da lógica fuzzy sobre o sistema em
estudo, verificando-se, inclusive, a importância do conhecimento das
necessidades do especialista (motorista) para o desenvolvimento de um
modelo. Os primeiros testes mostraram a possibilidade de um ganho de luz
significativo tornando a locomoção à noite mais segura para o motorista e para
o passageiro. Neste capítulo foram também levantados dados e informações
que estabeleceram uma primeira heurística ou conjunto de regras para
caracterizar quantitativamente os efeitos da velocidade e do raio de curvatura
sobre a luminosidade total necessária para os LEDs. O conjunto de regras
proposto foi modelado segundo estruturas fuzzy dos tipos 1 e 2. Os parâmetros
no conseqüente das regras para os diversos modelos propostos foram
estimados a partir dos dados coletados na pista de testes e alternativas de
otimização incluindo restrições na intensidade mínima de luz fornecida pelo
modelo foram testadas, aproximando o comportamento dos modelos ao
desempenho de iluminação desejado.
O capítulo 4 é uma extensão lógica do capítulo 3 e apresenta um
comparativo entre os comportamentos dos motoristas de carros e caminhões.
Uma pesquisa quantitativa sobre as necessidades de ambos os motoristas foi
feita e, na mesma pista de testes, foram medidos os dados de um carro e de
um caminhão com um equipamento chamado VBOX, que adquire a posição e a
velocidade do veículo em cada segundo. Neste capítulo é ressaltada e
11
comprovada a necessidade de um modelo específico para o controle da
iluminação veicular no caminhão. A pesquisa foi importante para destacar
pontos importantes em relação à iluminação veicular, como as necessidades
dos motoristas de caminhão e a falta de visibilidade nas estradas brasileiras.
O capítulo 5 apresenta a parte experimental do trabalho, que utilizou
dados, informações e resultados contidos nos capítulos anteriores, sobretudo
com relação aos modelos fuzzy desenvolvidos. Neste sentido, foi desenvolvido
um protótipo adotando o melhor LED de alta potência para esta aplicação, e
código em VB (Visual Basic) para simular o conjunto de regras fuzzy em teste
de bancada.
Por fim, o capítulo 6 destaca os resultados obtidos ao longo do trabalho
e as oportunidades que foram geradas para futuros trabalhos nesta linha de
pesquisa. O apêndice A detalha o depósito de patente e as suas
reivindicações, referentes ao trabalho desenvolvido.
Nomenclatura
AFS
sistema dianteiro adaptativo de iluminação
ECE
Comissão Econômica Européia
LED
diodo emissor de luz
SAE
sociedade de engenheiros automotivos
CONTRAN
Conselho Nacional de Trânsito
DENATRAN
Departamento Nacional de Transito
UNECE
Comissão Econômica das Nações Unidas para a Europa
12
Referências
Denatran, 2008. http://www.denatran.gov.br/resolucoes.htm.
Lukacs, L., 2006. The Impact of automotive design in the performance of
exterior vehicle lighting, SAE Technical Paper Series, No. 2006-01-2789.
Lukacs, L., 2007. The impact of adopting lighting technologies on emerging
markets, ISAL 2007 proceedings, Darmstadt, Germany.
Lukacs, L., Simões, L., Pepe, I., Embiruçu, M., 2007. Impact of adopting Fuzzy
logic to control an LED AFS (adaptive front-lighting system), SAE Technical
Paper Series, No. 2007-01-2784.
Neumann, R., 2006. LED front lighting – optical concepts, styling opportunities
and consumer expectations, SAE Technical Paper Series, No. 2006-010100.
UNECE, 2008. http://www.unece.org/trans/main/wp29/wp29regs41-60.html.
13
Capítulo 2
Estudo comparativo entre os LEDs: Golden Dragon
(Osram), K1 e K2 (Lumileds)
Apresentação
Este capítulo tem por objetivo definir qual o LED de alta potência e alta
eficiência que será utilizado como fonte de energia pra o modelo do AFS a ser
desenvolvido. Será feito um comparativo entre os três LEDs escolhidos a fim
de determinar o comportamento deste dispositivo e a sua adequação à
aplicação objetivada, ou seja, responder às demandas e exigências do sistema
adaptativo de iluminação veicular (AFS).
No capítulo anterior foram feitas a introdução e a definição do trabalho a
ser desenvolvido. O capítulo 3 será a continuação do desenvolvimento,
partindo para o entendimento do modelo que será adotado para controlar os
LEDs do sistema AFS. Ainda neste capítulo será demonstrada a melhor
alternativa de lógica funcional a ser adotada, nomeadas tipo 1 ou tipo 2, com
base nos dados que foram obtidos durante as simulações realizadas no
MATLAB.
Resumo
O processo de emissão de luz, numa pastilha de semicondutor, pela
aplicação de uma corrente elétrica é chamado eletroluminescência. A
eletroluminescência nos diodos emissores de luz (LEDs) tem lugar quando, em
uma junção P-N diretamente polarizada, ocorrem recombinações entre os
elétrons do lado N, que acabaram de ultrapassar a junção, com as lacunas do
14
lado P. Até o inicio dos anos 90, LEDs eram considerados fontes de energia de
baixo rendimento. Atualmente, aplicações automotivas com LEDs brancos
baseados em fluoróforos, com alto brilho, alta potencia e alta eficiência
energética, são extremamente promissoras, em especial em veículos e em
maquinários. A união entre geração eficiente de luz, longa vida útil e o fato de
que estes dispositivos continuam tendo o seu desempenho melhorado a cada
dia, faz do LED a fonte de luz do futuro.
Neste capítulo, três tipos de LEDs, de dois fabricantes diferentes, foram
estudados: "Luxeon" e "K2" da Lumileds e o "Golden Dragon" da Osram.
Palavras-chave: LEDs de alta potência, automotivo, LEDs, desempenho
Conclusão
No estudo comparativo entre os LEDs de alta eficiência, alta potencia da
OSRAM e Lumileds foram observadas algumas diferenças no comportamento
destes dispositivos. Os primeiros LEDs estudados foram: Golden Dragon, K1 e
K2. Para o K2 a curva de acomodação entre o regime transiente e o regime
estacionário só pode ser ajustada por duas exponenciais (dois tempos de vida),
enquanto a resposta do Golden Dragon pode ser ajustada por uma única
exponencial decrescente. Tanto o LED Golden Dragon quanto o K1 seguem
apenas uma exponencial da intensidade em função do tempo.
É importante salientar que o aparecimento de um decaimento da
intensidade na forma de uma dupla exponencial em função do tempo cria
certas restrições para o uso do K2 em regime contínuo. Entretanto, uma
análise mais detalhada da situação mostra que isso não impossibilita o uso
deste dispositivo em outras aplicações automotivas. Para um sistema dianteiro
15
adaptativo de iluminação (AFS, Adaptive Front lighting System) com LEDs, a
melhor alternativa é o Golden Dragon. Esta escolha justifica-se inclusive pelo
fato de que em muitas arquiteturas o controle de potência dos LEDs é feito por
modulação de largura de pulso, com pulsos muito mais curtos do que o tempo
de vida do decaimento mais rápido.
16
Chapter 2
Comparison study among LEDs: Golden Dragon
(Osram), K1 e K2 (Lumileds)
Opening statement
This chapter highlights which high power LED (high efficient) should be
chosen as the light source for the AFS model to be developed. A study will be
performed to compare 3 LEDs to define which one has the best performance to
this application and fulfills all needs for an adaptive front lighting system (AFS).
On the previous chapter, the opening statements and the definition of the
work that will be developed were presented. Chapter 3 will be the continuation
of this work, seeking the understanding of which model will be adopted to
control the LED AFS system. Chapter 3 will also show the best functional logic
alternative to be chosen, called type 1 or type 2, based on the data that was
obtained during the simulations held with MATLAB.
Abstract
The electroluminescence, a process where light is emitted by the
recombination of electrically accelerating charges (electrons and holes) on a
solid state chip, is the basis of light-emitting-diodes (LEDs) technology. Until the
early 90s, LEDs were considered light sources of low efficiency. Nowadays,
white high power, high efficiency LED’s, based upon fluorophores are extremely
promising for both vehicular / automotive and machinery usages. Associate with
17
its high efficient light production, the long life capability of these devices keep
improving their performance.
In this study, three models of LEDs, from two manufacturers were used:
"Luxeon" and "K2" from Lumileds and "Golden Dragon" from Osram.
Keywords: high power leds, automotive, LEDs, performance
18
2.1 Introduction
The lighting emission process, on a solid state semiconductor, using an
electric current (from an electrical power supply) is called electroluminescence.
The electroluminescence in the light emitting diode (LEDs) occurs when through
a P-N junction directly polarized, recombination between the electrons from the
N side takes place, from those electrons which surpassed the junction, with the
holes available in the P side. This recombination demands that the energy
acquired by the accelerated charges (electron and hole) needs to be lost again.
This energy loss can occur in two ways: through a non radioactive process by
phonons emission; heating the crystal semiconductor or through photons
relaxation by light emissions (Resende, 1996).
In silicon and germanium LEDs, the larger amount of energy is
dissipated through heat, the amount of light being emitted is insignificant and
that is why in the mid 50’s it was not possible describe the LEDs as an efficient
light source. However, in other materials that were either discovered or
combined in the mid 90’s, such as the gallium arsenide (GaAs), gallium
phosphorus (GaP), gallium nitride (GaN), indium gallium nitride (InGaN) among
others, the electroluminescence process is very efficient and the amount of
photons emitted is sufficient for LEDs to become an excellent light source
(Levinshtein et al, 2001).
During almost three decades, LEDs were associated with lightning pilot
bulbs, assembled on the instrument panels serving as status displaying, limited
to red, yellow and green. In 1993, a Japanese company, Nichia Chemical,
started the production of blue LEDs, combining red, green and blue to produce
19
white light. This was a breakthrough discovery, and started a complete new field
of research regarding this technology (Nakamura, 1996).
In parallel to the Nichia technology, there were also developments
regarding blue and violet LEDs, which allowed the production on white light,
based upon the insertion of a fluorescent substance. These devices are based
on the Sttokes shift, where the substances absorb small wavelengths (blue and
violet) and emit light in longer wavelengths such as green, yellow and/or red.
Regarding this, it is strategic to highlight that it is different from fluorescent
materials that are bright in the dark. Fluorescent materials will not emit when the
light is no longer available (Kittel, 2004).
The white light LED application, based upon fluorophores, seems to be
promising regarding not only the public and residential applications, but also
vehicle illumination and equipment in general (Nakamura, 1996; Zukauskas et
al, 2002; Takahashi et al, 2007).
The benefits of high performance linked with a long life span (at least 25
times more than a conventional bulb and 10 times more than a discharge gas
lamp) make this an excellent alternative for future developments.
In addition, there is also a consensus regarding the new semiconductor
materials, which through the improvements on the internal structures of this
high power LEDs, will cause a huge growth in this technology in the next
decade, particularly in the light output of the device (Wördenweber et al, 2007).
In this study, there will be a comparison of the light output through time among
three commercial LEDs.
20
2.2
Experimental study
Figure 2.1 presents the experimental device developed to measure the
light intensity from commercial LEDs. To ensure the robustness and
repeatability to this set up, an optic rail (granite & aluminum) was used, that
served as the base to attach the different elements used during measuring. In
this rail there are three different apparatus attached: the LED that will be tested,
the aluminum LED heat sinker and a MLM 1332 photometer from Minipa, with
an analogical output that supplies a tension proportional to the light intensity.
Connected to the photometer output there is a digital multimeter with an RS232
port, allowing connection and automatically data taking by a personal computer
(PC).
Figure 2.1: Experimental device: 1) LED under analysis, 2) heat sinker, 3a and
3b) photometer, 4) aluminum and granite optic rail, 5) constant current supply,
6) multimeter with serial output, 7) PC.
21
2.3
Method and materials
The measurements were performed with 3 different LEDs from 1.5 to 3.5
W, K1 and K2 from Lumileds and Golden Dragon from OSRAM were the
chosen ones. Initially, the LED to be measured was already assembled to the
heat sinker; is precisely positioned in a pre-determined position in the granite
rail. The distance between the LED external surface and the photometer is 60
mm. This distance was kept constant through the entire process of data
acquiring. The axis that crosses the center of the LED and the center of the
photometer is defined as Z, while the perpendicular axis which is tangent to the
external LED surface is called Y (Figure 2.2). The alignment in relation to the Y
axis is done manually, pursuing the maximum photometer illumination. The next
step, once the LED is already positioned related to Z, is to light it and move it
parallel to the Y axis in order to maximize the light collection. This procedure
was necessary due to the fact that glueing the LED to the heat sinker ensures a
perfect centralization of this element. That is why the aluminum dissipator
cannot be used as a reference during the LED positioning phase, as well as the
LED external package.
22
Figure 2.2: Details from the set up: 1) aluminum rail, 2) positioning platform, 3)
LED assembled to the aluminum heat dissipater, 4) photometer. The distance d
is 60 mm.
The LED light emission intensity data was acquired adopting the
following methodology:
•
A PC based program acquires the data, with three measurements per
second rate, from a digital multimeter connected to its serial port. RS232
protocol is used as the communication link between the PC and
multimeter. This program was written using BASIC (Russo e Echols,
1999);
•
Initially the LED is off, the program is initiated and only after 15 seconds
the current power supply is turned on, which ignites the LED. Following
this procedure, two operational details must be guaranteed: first, the
acquiring system must be stable, when the LED is on; and second, a
synchronizing circuit (trigger) must not be necessary to ensure that the
23
LED has been turned on. The sequence of how the data was obtained
can be seen in Figure 2.2.
This photodiode light observation procedure enables the study, not only
of the LED stationary regime, but also of the initial part of the process,
characterizing the transient.
2.4
Results
Emitted light intensity data from three different LEDs ((K1) and (K2) from
Lumileds; (Golden Dragon from Osram) as a function of time is presented on
Figure 2.3. It shows that, in all cases, the transient regime is well defined, which
is more perceived in the K2 LED.
35000
Light Intensity (arb. units)
30000
K2
Osram
25000
K1
20000
15000
10000
5000
0
0
15
30
45
60
75
90
105
120
135
150
Time (s)
Figure 2.3: Emitted light intensity generated by 3 different LEDs ((K1) and (K2)
Lumileds; (Golden Dragon from Osram) over time.
24
In order to better analyze the accommodation period of the light output,
the initial part of the data acquired (first 15 s) was not taken into consideration.
In this stage, the LEDs are off. Removing the initial instants from the curves on
Figure 2.3 and subtracting a constant value of 10 from the offset (Figures 2.4,
2.5 and 2.7), one can view the output intensity behavior of each LED. The first
LEDs studied were the Golden Dragon and K2, as shown on Figures 2.4 and
2.5. The curve for the Golden Dragon data can be adjusted by one decreasing
exponential, while the K2 needs two exponentials, with two accommodation
lifetimes. Table 2.1 presents the fitting parameter for the three LEDs
accommodation lifetimes.
Particularly, on the K1 case, this device was compared with the K2 since
both of them have the same internal construction and belong to the same
manufacturer.
Table 2.1: Emitted light intensity accommodation period from a high Power LED
Accommodation
period t1 (s)
Accommodation
period t2 (s)
Osram Golden
Dragon
3.414 +/- 0.025
Lumileds K2
Lumileds K1
4.661 +/- 0.059
5.020 +/- 0.090
-
42.991 +/- 0.498
45.400 +/- 1.900
25
0.250
Osram
Golden Dragon
Intensity (arb. units)
0.248
0.246
0.244
0.242
t1 =(3,4136 +/- 0,02478) (s)
0.240
0.238
0
20
40
60
80
100
120
140
160
Tim e (s)
Figure 2.4: Emitted light intensity response curve time related (OSRAM Golden
Dragon). In red, the experimental point adjustment through a single decreasing
exponential.
0.34
K2
t1 = 4,66064 +/- 0,05934 (s)
intensity (arb. units)
0.32
0.30
0.28
t2 = 42,99084 +/- 0,49795 (s)
0.26
0.24
0
20
40
60
80
100
120
140
Time (s)
Figure 2.5: Emitted light intensity response curve time related (Lumileds K2). In
blue, the experimental point adjustment through a double decreasing
exponential.
26
When comparing the curves on Figures 2.4 and 2.5, the conclusion is
that the OSRAM Golden Dragon LED has a simpler stabilization profile through
time, with a first order system behavior. Soon after the overshoot in the signal,
during the LED polarization, the system shows certain stability (3.414 s) and
stays permanent on regime. On the other hand, the Lumileds K2 LED presents
a more complex light output profile through time, since after a quick
accommodation (4.661s), which is slower than the Golden Dragon, this device
continues to lose its light intensity progressively.
Formally speaking, K2 will only have one light output, considerably
stable, after approximately 7 lifetimes (300 seconds).
It is important to highlight that the K2 was presented to the market as a
high power light output, with innumerous potential applications. With the
domestic public and even automotive application, the K2 (LXK2-PW12-S00) is
driven up by 1 A (ampere) and promises a 100-lumen flux output. This is
compared to the 350 mA applied to the OSRAM Golden Dragon (ZW W5SGJZ), which generates 70 lumens of light.
On the present study, the comparison to be taken into consideration is
that almost 33,000 lux are generated by the K2 at 60 mm from the photometer
surface in relation to the 25,000 lux that are generated by the Golden Dragon at
the same distance. However, this 35% difference between them only lasts some
milliseconds, changing right after the polarization period for both devices. After
this initial period, this difference decreases, as shown in Figure 2.6.
In this figure, the experimental points were obtained through the ratio of
the light output through time from K2 and Golden Dragon as well. In less than
150 seconds of functionally, the difference is reduced to 5 %, while the energy
27
balance remains at the 300% level, very much in disfavor of the Lumileds
product.
Relative Intsnsity K2/GoldenDragon
1.35
1.30
1.25
1.20
1.15
1.10
1.05
0
20
40
60
80
100
120
140
Time (s)
Figure 2.6: Ratio between light supplied by LED K2 in relation to the light
supplied by LED Golden Dragon over time.
28
0.227
t1 = (5.02 +/- 0.09) s
Intensity (arb. units)
0.226
0.225
0.224
t2 = (45.4 +/- 1.9) s
0.223
K1
0.222
0
50
100
150
200
250
Time (s)
Figure 2.7: Light intensity curve as a function of the time related (Lumileds K1).
In blue, the experimental point adjustment through a double decreasing
exponential.
Figure 2.8, supplied by the K2 manufacturer (Lumileds, 2007), presents
the interdependence between the junction temperature and light output
generated by the LED. Adopting as a temperature reference 22 0C, and a
constant temperature inside the laboratory where the measurements were
taken, a 30% loss of performance could only be explained if the junction
temperature would be higher than 150 0C in less than 2.5 minutes, the total time
used to acquire the data.
29
Figure 2.8: Lumileds LED relative light output versus junction temperature
(Lumileds,2007).
Regarding the heat evacuation ceramics in which the LEDs are
assembled and associated with the aluminum heat sinker weight of
approximately 100 g, the total energy (E) given to the ensemble is equal to the
electric power multiplied by time Q= 490 Joule. As the aluminum specific heat is
Cal = 0.9 J/g 0C, in a system without losses, where all the energy would be
converted into heat, the ensemble temperature should be Tf = (Q+ Cal·m·Ti) /
(Cal·m) 0C, which results in Tf = 30.4 0C when evaluated.
It is hard to assume, under reasonable thermodynamic conditions, that
the junction temperature can be 140 0C, since the heat sinker stays at 40 0C.
The rise of the junction temperature explanation is more unlikely when
compared with the K1 data results. This device works with one third of the K2
power consumption, but presents the same behavior regarding the two
exponentials. In addition to that, its second life time component is compatible
within less than one sigma from the results found on the K2.
30
Still, while trying to decipher the K2 behavior, there were additional
measurements done with different polarized currents, which in principle should
determine different temperatures on the LED junction. In Figure 2.9 one can see
that the behavior is similar, and despite different polarization currents applied,
the K2 accommodation profile is still the same.
0.35
Light Intensity (arb. units)
0.30
1000mA
0.25
700mA
0.20
500mA
0.15
350mA
300mA
250mA
0.10
0.05
K2
0.00
-0.05
-20
0
20
40
60
80
100
120
140
160
Time (s)
Figure 2.9: K2 light intensity over time with different polarized current.
Applying the same kind of analysis on the data in Figure 2.9, adjusting
the experimental points to two decreasing exponentials; it is possible to
establish a second life time accommodation light output profile from the K2 LED
in relation to the polarization current. Figure 2.10 shows results of this analysis.
31
45
40
Stabilization time (s)
35
30
25
20
15
10
5
0
200
400
600
800
1000
K2 foward current (mA)
Figure 2.10: Second life cycle for K2 LED light output profile accommodation
timing in relation to the polarization current of the device.
2.5
Conclusion
In this chapter, the comparison among high power, high efficiency LEDs
from OSRAM and Lumileds has shown some differences between the
behaviors of the devices. Three LEDs were tested: Golden Dragon, K1 and K2.
The major difference is perceived on the K2, since the Golden Dragon light
output versus time curve can be adjusted by a single decreasing exponential
and K2 can only be described by a double exponential decay with two
accommodation periods. Both K1 and Golden Dragon have only one
exponential through time.
It is of utmost importance to highlight that this singular behavior can be
the deciding factor not to adopt this LED for continuous powering application,
but the K2 remains a possible candidate for other automotive applications. For
an AFS application, the Golden Dragon is the best alternative. This is justified
32
since for many architectures the power LED control is done through pulse width
modulation, with a pulse width shorter than the most short decay time.
References
Kittel, C., 2004. Introduction to Solid State Physics, 8th Edition. Ed Wiley
Interscience, NY, USA.
Levinshtein, M., Rumyantsev, S., Shur, M., 2001. Properties of Advanced
Semiconductor Materials: GaN, AIN, InN, BN, SiC, SiGe, Wiley-Interscience,
USA.
Lumileds datasheet, 2007. -- http://www.lumileds.com/pdfs/DS51.pdf.
Nakamura, S., 1996. High-brightness blue/green LEDs and first III-V nitridebased laser diodes, Proc. SPIE Physics and Simulation of Optoelectronic
Devices 2693, 43-56.
Resende, S.,1996. A Física de Materiais e Dispositivos Eletrônicos, UPFE,
Editora da UFPE.
Russo, M., Echols, M. 1999. Automating Science and Engineering Laboratories
with Visual Basic. Wiley Interscience, NY, USA.
Takahashi,
K.,
Yoshikawa,
A.,
Sandhu,
A.,
2007.
Wide
Bandgap
Semiconductors: Fundamental Properties and Modern Photonic and
Electronic Devices. Ed. Wiley, New York.
Wördenweber, B., Wallaschek, J., Boyce, P., Donald D. Hoffman, D., 2007.
Automotive Lighting and Human Vision, Springer Verlag, USA.
Zukauskas, A., Shur, M., Caska, R., 2002. Introduction to Solid-State Lighting,
Wiley Interscience, NY, USA.
33
Capítulo 3
Benefícios e desafios de controlar um AFS (Adaptive
Front-lighting System, sistema adaptativo de iluminação
veicular) a LEDs adotando a lógica fuzzy
Apresentação
Com base na definição e nas características do tipo de LED adotado neste
trabalho, e discutido no capítulo anterior, neste capítulo são desenvolvidos e
analisados modelos baseados em lógica fuzzy para o controle de um sistema
adaptativo de iluminação veicular dianteira a LEDs. É importante salientar que o
termo adaptativo é aplicado aqui ao fato do modelo se adaptar às necessidades
do motorista, e não de ser um modelo que sofre uma mutação ou alteração
conforme o seu aprendizado. O conjunto de regras, as condições de contorno
(pista de testes), o conhecimento do especialista, a estrutura fuzzy adotada (tipos
1 e 2), as variáveis de entrada e as respectivas funções de pertinência são
apresentados e discutidos juntamente com os resultados, evidenciando-se o
desempenho dos modelos desenvolvidos no controle da intensidade de iluminação
dos LEDs, de acordo com a velocidade do veículo e o raio de curvatura da
rodovia.
O problema de controle compreende a determinação de um valor
satisfatório de quantidade de iluminação, a ser promovida através dos LEDs, que
deverá ser determinado continuamente em função das medições do raio da
curvatura e da velocidade do veículo. Neste sentido, foram desenvolvidos modelos
34
ou sistemas de inferência fuzzy segundo Takagi-Sugeno (Liang and Mendel, 2000;
Mendel, 2001), tendo-se o raio da curvatura da pista e a velocidade do veículo
como entradas (nos antecedentes das regras) e a corrente elétrica total para os
LEDs como saída, com o consequente aumento do número de LEDs acesos, e
portanto do nível de iluminação. Foram testados diferentes estruturas para os
conseqüentes no âmbito das alternativas oferecidas pelas lógicas fuzzy dos tipos
1 e 2. Um conjunto de regras original foi gerado com base no conhecimento de um
especialista e este conjunto serviu como estimativa inicial para o ajuste dos
parâmetros dos modelos fuzzy propostos através de um procedimento de
minimização dos erros entre a predição do modelo e a resposta do especialista.
Os resultados demonstraram o potencial da metodologia proposta e a sua
viabilidade para o controle da iluminação, proporcionado uma direção noturna com
mais segurança de acordo com as necessidades do motorista e do mercado.
Este capítulo está plenamente contextualizado neste trabalho de tese. Em
primeiro lugar, o desenvolvimento e os resultados contribuíram para definir o
modelo adequado para controlar os LEDs do sistema AFS, demonstrando, neste
caso, qual a melhor alternativa de lógica fuzzy a ser implementada (tipo 1 ou tipo
2). Em segundo lugar, este capítulo representa uma extensão racional do anterior
onde foi apresentada a definição e a caracterização do tipo de LED de alta
potência. Além disso, este capítulo oferece as bases metodológicas para o
desenvolvimento de modelos semelhantes para aplicação na iluminação veicular
de caminhões, assunto este abordado no próximo capítulo onde são também
discutidos e apresentados os resultados e a análise de uma pesquisa quantitativa
que auxilia na caracterização do comportamento e das necessidades de
35
motoristas (carro e caminhão) em relação aos efeitos do meio ambiente e das
estradas sobre a definição da melhor condição de iluminação noturna.
Resumo
A iluminação veicular tem tido, nas ultimas décadas, avanços tecnológicos
expressivos como o uso de diodos emissores de luz (LED, Light Emitting Diode)
em sistemas adaptativos de iluminação veicular dianteira (AFS, Adaptive Frontlighting System), que representam uma grande inovação tecnológica na indústria
mundial no viés da iluminação veicular. Este capítulo apresenta alternativas de
controlar um sistema AFS utilizando modelos fuzzy (tipos 1 e 2) para determinar
os parâmetros de trabalho, levando em consideração não só as necessidades do
motorista, mas também as condições das estradas no estado de São Paulo
(Brasil). A lógica fuzzy é uma conhecida extensão da lógica booleana que permite
o tratamento de incertezas na informação através da definição de funções de
pertinência com valores entre "totalmente verdadeiro" e "totalmente falso". Esta
técnica, ou estratégia de modelação, é particularmente importante quando uma
decisão de vários parâmetros necessita ser feita ou quando as decisões são
baseadas no conhecimento humano. Os resultados demonstram o potencial da
metodologia proposta e sua pertinência para o controle de iluminação,
proporcionado uma direção noturna com mais segurança, de acordo com as
necessidades do motorista e do mercado.
Palavras-chave: lógica fuzzy, AFS, LED, controle, iluminação
36
Conclusão
Este capítulo descreve o desenvolvimento da aplicação do sistema
baseado nas regras fuzzy, para modelar e prever a quantidade de luz a ser
adicionada ao motorista durante o período de direção noturna. O modelo proposto
engloba uma combinação entre a experiência do especialista e o processo de
estipulação dos dados que permitem o ajuste do modelo final de cada regra,
considerando as diferentes estruturas para fuzzy do tipo 1 e do tipo 2. Mais
especificamente, o julgamento do especialista pode ser apropriadamente
incorporado à base de regras fuzzy de acordo com o seu conhecimento em
relação à direção noturna e às condições do meio ambiente. Este modelo, apesar
de ter sido desenvolvido com dados retirados de uma pista de testes, pode ser
adotado para quaisquer condições de rua e meio ambiente, como avenidas,
estradas e o trânsito nas cidades, para mencionar alguns exemplos.
A Figura 3.8(b) demonstra a superfície modelada que apresenta o aumento
de corrente quando as curvas e velocidades diminuem e a diminuição de corrente
quando as curvas e velocidade aumentam. Também destaca uma superfície mais
suave quando esta é comparada ao modelo 2, especialmente perto do raio de 60
metros de curvatura, onde observa-se uma ruptura abrupta criada pela flutuação
do modelo. Nesta área em especifico, perto dos 60 metros, é muito importante
existir uma superfície suave, devido ao fato que a percepção visual é obtida nas
extremidades das curvas, ajudando o motorista à noite.
Alguns modelos são adequados para a aplicação prática, entretanto a
melhor alternativa é o modelo 4 que considera uma restrição que garante o
conhecimento do especialista da melhor forma. Estes modelos têm respostas de
37
saída similares ás do especialista, realçando e reiterando a importância de
oferecer uma dirigibilidade noturna mais segura, fornecendo mais luz para o
motorista. Apesar destas soluções não fornecerem o menor erro quadrático, elas
permitem que o motorista enxergue mais à noite, devido ao fato de oferecerem
praticamente a mesma quantidade de corrente que o especialista preconiza. A
idéia é que o especialista tem um profundo entendimento das necessidades do
motorista devido ao seu conhecimento e à sua experiência.
Finalmente, é importante destacar que o uso dos LEDs é justificado devido
a certas vantagens que eles têm quando comparado com o sistema halogeno.
Não apenas em relação a uma vida útil maior, um consumo de energia menor e
uma temperatura de cor melhor, para mencionar algumas, mas também em
relação às tendências de projeto e aparência em torno deste conceito.
Investigações adicionais no futuro serão perseguidas para melhorar o algoritmo e
o sistema. Além disso, demonstra-se que as novas tecnologias podem melhorar
não só a aparência veicular, mas também o desempenho geral do sistema de
iluminação.
38
Chapter 3
Benefits and challenges of controlling a LED AFS
(Adaptive Front-lighting System) using fuzzy logic
Opening Statement
Based on the definition and characteristics of the chosen LEDs in this study,
and already discussed in previous chapter, this chapter develops and analyzes the
models based on fuzzy logic to control an LED AFS system. The rules, road
conditions (test track), specialist knowledge, fuzzy type-1 and type-2 structures,
input data and the pertinence functions associated are presented and discussed
with the results, highlighting the model performance taking into consideration the
lighting intensity of the LEDs according to the vehicle’s speed and road curvature.
The controlling issue embraces the definition of a suitable value of the
amount of light to be provided by the LEDs in which should be determined in a
continuous pattern taking the functions that correspond to the measured radius and
vehicle speed. In order to achieve this, models or fuzzy systems were developed
according to Takagi-Sugeno (Liang and Mendel, 2000; Mendel, 2001) adopting the
track curvature’s radius and vehicle’s speed as inputs (antecedents rules) and the
total LED current as the output, as a result the number of lit LEDs and the increase
of illumination. Different structures were tested regarding the consequents within
the available alternatives for fuzzy type-1 and type-2. An original set of rules was
created based upon the specialist’ knowledge and this set was adopted as a
starting point to adjust the proposed fuzzy models according to a procedure which
39
seeks the small quadratic error between the calculated model and the specialist’s
response.
The results have shown the potential of the proposed methodology and its
viability to control the illumination, providing a safer nighttime driving fulfilling the
driver and market needs.
This chapter is fully aligned with the thesis’ work. First, the development and
results obtained contributed to define the more adequate model to control the LED
AFS system, showing, in particular, the best alternative for the fuzzy logic to be
implemented (type-1 or type-2). Also, this chapter embraces a rational extension of
the previous chapter in which the high power LED definition and characterization
was presented. Moreover, this chapter also presents the basis of the methodology
to develop similar models which will be applicable to heavy trucks lighting
developments. This subject will be discussed in the next chapter together with the
analysis of a quantitative survey which supports the characterization of the driver’s
behavior (car and truck) taking into consideration the environmental and road
conditions to define the best night time driving scenario.
Abstract
The vehicular illumination system has had, in the past decades, expressive
technological advances such as the use of a Light Emitting Diode (LED) Adaptive
Front-lighting System (AFS), which represents an industry breakthrough in lighting
technology and is rapidly becoming one of the most important innovative
technologies around the world in the lighting community. This chapter presents
40
AFS control alternatives using fuzzy logic (types 1 and 2) to determine its operating
parameters taking into consideration both the driver’s and road conditions from the
state of São Paulo (Brazil). Fuzzy logic is a well-known extension of the
conventional (Boolean) logic that enables the treatment of uncertainty presented in
the information through the definition of intermediary membership values between
the "completely true" and the "completely false". This technique or modeling
strategy is particularly important when a multi-parameter decision must be taken or
the decisions are based on the human knowledge. The results show the
potentiality of the methodology proposed and its suitability for light control,
providing a safer nighttime drive more safe according to the driver’s and market
needs.
Keywords: fuzzy logic, AFS, LED, control, lighting
41
3.1
Introduction
The car lighting system of the car market today is still based on the halogen
system as shown in Table 3.1, which also foresees that halogen will predominate
for the next 5 years (Stern, 2008).
Table 3.1: Global market prevalence of halogen headlamps, 2007-2013 (fleet
percentage)
Prevalence
2007
2008
2009
2010
2011
2012
2013
84
84
83
83
80
79
77
Source: just-auto, industry estimates (Just Auto, 2007)
Despite the appearance of steerable auxiliary headlamps in the 1940s and
steerable main headlamps in the 1960s, regulations worldwide have for many
years required that all vehicle headlamps have to remain laterally rigidly mounted.
This is no longer the case, as AFSs have changed that paradigm radically and are
being developed and deployed globally at an unprecedentedly rapid pace
(Neumann, 2004).
All low-beam light distributions have long been asymmetrically biased to
control glare, at the expense of seeing distance through curves and turns. The
basic AFS function, called bending light, swivels the headlamp beams (or optically
shifts light within the beam) in response to the driver’s steering wheel movements
and/or input from an onboard gyroscope. Bending light significantly increases the
42
distance reach of the low beam in curves, without increased glare to oncoming
traffic (Ishiguro and Yamada, 2004).
The AFS approach that has gathered the most commercial interest is curve
lighting. It involves, in some embodiments, only horizontal or vertical displacements
of a given base beam pattern. Specifically, in these embodiments, curve lighting
(also called bending or swiveling light) involves controlling the horizontal aim of the
beam pattern (or a part of it), depending on variables such as the radius of the
curve and the speed of the vehicle. There are several possible strategies for
implementing curved lighting, including moving both lamps in tandem, moving both
lamps but each to a different extent, moving only one of the lamps, moving lamps
differentially on left and right curves, or energizing additional lamps (Hamm, 2002;
Hogrefe, 2000; Reich and Rummel, 2001).
Globally there is a huge increase for the introduction of LEDs due to the fact
of low current consumption and long mean time between failures (high MTBF). A
HID (high-intensity discharge) headlamp burners produce between 2,800 and
3,500 lumens from between 35 and 38 watts of electrical power, while halogen
filament headlamp bulbs produce between 700 and 2,100 lumens from between 40
and 72 watts at 12.8 V (UNECE, 2008). The first series-production LED headlamps
were factory-installed on the Lexus LS 600h / LS 600h L starting with the 2008
models. Low beam, front position light and sidemarker are performed by LEDs;
high beam and turn signal functions use filament bulbs. The headlamp is supplied
by Koito. Full-LED headlamps supplied by AL-Automotive Lighting were fitted on
the 2008 V10 Audi R8 sports car except in North America. The Hella headlamps on
the 2009 Cadillac Escalade Platinum became the first U.S. market all-LED
43
headlamps. Present designs give performance between halogen and HID
headlamps (Hella, 2009), with system power consumption slightly lower than other
headlamps, longer life spans and more flexible design possibilities (Hella, 2009;
AL, 2009). As LED technology continues to evolve, the performance of LED
headlamps is predicted to improve to approach, meet, and perhaps one day
surpass that of HID headlamps. The application of LED technology significantly
contributes to the avoidance of CO2 emission and the reduction of fuel
consumption.
This aspect especially gains importance with the implementation of daytime
running light (DRL). DRL with LED needs 14W of energy, whereas the use of
conventional vehicle lighting at day (low beam, rear lights, position lights)
consumes 300W (AL, 2009). Table 3.2 presents the trend for the LED
implementation in some regions and it is important to mention that the emerging
markets will grow in the next 5 years as well (Zukauskas et al., 2002; Stern, 2008).
Table 3.2: Global market value by region for automotive LEDs, 2007-2013 (US$m)
Region
2007
2008
2009
2010
2011
2012
2013
North America
123.7
126.6
128.7
130.1
134.3
136.6
137.5
Europe
116.0
119.7
122.6
126.3
128.2
134.7
143.1
Japan
75.6
76.9
78.3
79.2
81.0
81.2
83.2
Rest of world
7.7
7.7
7.9
8.0
8.2
8.3
8.4
Source: just-auto, industry estimates (Just Auto, 2007)
44
This table also shows a huge progress regarding the effectiveness and
performance of white LEDs which is the state of the art for this application. In
addition, being able to handle the thermal management is definitely one of the key
challenges in automotive application due to its high temperature (Kittel, 2004;
Levinshtein et al., 2001).
The main purpose of this chapter is the development and application of a
fuzzy algorithm system based upon the specialist expertise to control an array of
LEDs. Simulation tests using real data of speed and curvature radius (geometric
features of the track) show that the model provides an increase of light on the
curves according to the road and driver’s need making nighttime driving safer
(Gotoh and Aoki, 1996). This system adds light to the beam pattern which is
different from the bending light approach which does not add light to the night
drive, but swivels it. The aspects associated to the fuzzy modeling and its purpose
are presented (section 3.2) as well as the device decision and controlling template
(section 3.3). Some models based on the fuzzy logic (types 1 and 2) were
developed and applied to the LED control. The rules were defined according to the
specialist knowledge (section 3.4). The specialist, who is an expert in the
automotive lighting field with more than 15 years of experience developing lighting
systems in the auto industry, understands the environment (city, motorway and
rural) and road conditions such as pedestrians, fog and rain, for example, in the
region which is used in the model. As a reference, Schmocker et al. (2008) did a
study regarding the fuzzy impact of traffic control on an urban area, as the expert
knowledge will have a huge impact in this process of modeling the rules in order to
optimize the traffic around the city. Here, the specialist will have the impact on
45
determining the initial model. Further in section 3.4, the model parameters (i.e.,
membership function parameters associated to the consequents of each rule) were
estimated through the comparison between the specialist responses (desired
responses) and the model results, using an amount of data related to the driver’s
and road conditions of the South American target customer (Wördenweber et al.,
2007). This procedure allowed the selection of parameter values that can provide
optimized solutions, considering that the expert’s knowledge may not always
involve practical rule-of-thumb guidelines concerning the appropriate estimation of
these parameters. In sections 3.5 and 3.6 results and concluding remarks are
presented and discussed, respectively.
3.2
Fuzzy knowledge base and membership functions
Fuzzy logic is a superset of conventional (Boolean) logic that has been
extended to handle the concept of partial truth - truth values between "completely
true" and "completely false", particularly important when a multi-parameter decision
must be taken (Bellman and Zadeh, 1970). The most important feature of fuzzy
control is that its internal mathematical model is built-up directly through expertise
from human reasoning which can be expressed by a set of heuristic rules which
are quantified according to fuzzy set theory. In this sense, fuzzy control algorithms
and fuzzy inference systems present many advantages (Altinten et al., 2003) such
as simplicity, robustness, no need to find transfer functions, nonlinear behavior and
adaptability.
A type-1 fuzzy set is a generalization of a crisp set or zero-one membership
function (Mendel, 2001). A crisp set A, denoted by µA(x), is such that:
46
1 if x ∈ A
A = µ A (x ) = 
0 if x ∉ A
(1)
A type-1 fuzzy set F is defined on a universe of discourse X (the range of
possible values for the variable x) and its membership function, denoted by µF(x),
takes on values in the interval [0, 1]. A membership function provides a measure of
degree of similarity of an element in X to the fuzzy set F. The rules of a linguistic
fuzzy model have the general form:
If x is A then y is B
(2)
The proposition “x is A” is the antecedent of the rule, and the proposition “y
is B” is the consequent. Variables x and y are linguistic ones defined as fuzzy sets
on domains (universe of discourses) X and Y, respectively. Constants A and B are
linguistic terms usually associated with meanings for the linguistic variables, such
as “low temperature”, “high velocity”, etc. The rule base together with the database
forms the knowledge base of the fuzzy system (Babuška and Verbruggen, 1996;
Chen and Liu, 2004). The input-output mapping is accomplished by the fuzzy
inference mechanism. The main approaches in this case are the Mamdani and
Takagi-Sugeno-Kang (TSK) models (Liang and Mendel, 2000). The Mamdani
model has fuzzy sets in the consequents, while in the TSK model the consequents
are parametric models of the inputs (Mendel, 2001).
The type-2 fuzzy logic was introduced by Zadeh (1975) as an extension of
traditional fuzzy logic. Subsequently, several researchers presented and developed
important contributions such as the works of Mizumoto and Tanaka (1976, 1981),
Dubois and Prade (1978, 1979), Hisdal (1981) and Karnik and Mendel (1998,
2000). The origin of the type-2 fuzzy logic is related to the inability of the traditional
47
fuzzy logic to treat completely the uncertainty associated to the information
(Mendel, 2003). In a type-1 fuzzy set, the membership grade of a variable x (x ∈ X,
where X is the universe of discourse), is represented by µA(x), which is only a crisp
number in [0, 1]. A type-2 fuzzy set à has a membership grade of x (x ∈ X) in Ã
represented as µÃ(x) which is a type-1 fuzzy set in [0, 1]. Therefore, in a type-2
fuzzy set, an element of its domain does not have a single value as membership
degree. The element is associated with another membership function (Karnik and
Mendel, 1998) that represents the uncertainty related to the definition of the
membership degree associated to a specific value of the linguistic variable x.
A generic rule in a type-2 fuzzy model has the same form presented in (2).
The differences between these techniques are centralized in the fuzzy sets
definition. For the specific case of TSK model, the general structure for a type-2
fuzzy model comprises the use of type-2 fuzzy sets in the antecedents together
with type-1 fuzzy sets to represent the consequent parameter. Therefore, these
rules simultaneously account for uncertainty about antecedent membership
functions and consequent parameter values (Mendel, 2001). Specifically, the first is
usually associated with the situation on which more than one specialists suggest
different membership functions for each one of the inputs (Hagras, 2004; Tahayori
et al., 2006). Moreover, special cases occur when the antecedents are type-2 fuzzy
sets but the consequents are crisp numbers and when both antecedents and
consequents are type-1 fuzzy sets (Mendel, 2001).
Despite the fact that type-1 fuzzy logic has been used in several production
processes since the 1970s (Østergaard, 1977; King and Mamdani, 1977) and that
48
the fuzzy systems are universal function approximations (Sala et al., 2005), many
areas of human knowledge have also used type-2 fuzzy inference systems (Li et
al., 2006; Pareek and Kar, 2006; Ren et al., 2007; Mendoza et al., 2007; Thovutikul
et al., 2007; Du and Zhu, 2006). As examples to be highlighted, there are several
applications for fuzzy logic, in particular, fuzzy logic has been adopted to control
traffic in the cities (Anderson et al., 1998; Niittymäki and Turunen, 2003; Kosonen,
2003). Anderson et al., describe the investigation made into the feasibility of
optimizing a prototype fuzzy logic signal controller with respect to several criteria
simultaneously. The controller's sensitivity to changes in the membership function
parameters was demonstrated and it was not possible to minimize simultaneously
even the limited set of performance measurements explored (travel times and
emissions). These results indicated that a multiobjective genetic algorithm
optimization technique is appropriate for further research. On the other hand,
Niittymäki and Turunen (2003) main intention in their study is to tie fuzzy reasoning
to many-valued logic framework. A Lukasiewicz many-valued logic similarity based
fuzzy control algorithm is introduced and tested in three realistic traffic signal
control systems. The results are compared to fuzzy control systems where the
inference is based on standard Matlab Fuzzy Logic toobox's Mamdani-style
system. The compared traffic signal control modes are signalized pedestrian
crossing and multi-phase signal control with phase selection. As per Kosonen’s
approach, a traffic signal control system based on real-time simulation, multi-agent
control scheme, and fuzzy inference is presented. This system called HUTSIG is
closely related to the microscopic traffic simulator HUTSIM, both have been
developed by the Helsinki University of Technology. The HUTSIM simulation model
49
is used both for off-line evaluation of the signal control scheme and for on-line
modeling of traffic situations during actual control. Indicators are derived from the
simulation model as input to the control scheme. In the presented control
technique, each signal operates individually as an agent, negotiating with other
signals about the control strategy. Here the decision making of the agents is based
on fuzzy inference that allows a combination of various aspects like fluency,
economy, environment and safety. Nevertheless, it is important to highlight that so
far, no one has adopted this fuzzy approach (type-1 or type-2) to control a LED
AFS system. The examples mentioned adopt different approaches to the fuzzy
implementation.
3.3
The lighting device and hardware proposed
Throughout the entire automotive industry the lighting system has always
had a very important role to play during its long history. In the past 50 years,
vehicular lighting has achieved an important status due to its close relationship with
enhancing passenger and vehicle security. Naturally, this interwoven scenario
affects both the design studio and how the best technical solution can be aptly
translated into the vehicle's "eyes", while maintaining the brand identity, a key
aspect of every vehicle.
Several technological advances have been introduced over the years, such
as double beam systems (one for low and one for high beam displacements in the
1950s) and the introduction of xenon headlights in the 1970s. Although the xenon
technology has been available for more than 20 years, it does not represent 20%
50
of the entire worldwide market because of its high cost and regulatory implications
on Economic Commission for Europe (ECE) markets (auto leveling and washer
system). In the late 1990s, the use of infrared technology, along with state-of-theart reflectors, has allowed the driver to experience a better night drive than he ever
could before. Lately, many changes inside the lighting community have been
implemented due to relatively cheaper technology, specific customer requests and
higher market expectations. LEDs have been the "trend" pushed forward by design
studios around the world, among others because it allows the studio to create a
design trend around this concept (Nakamura, 1996). However, due to cost and
heat management issues its overall performance has not yet reached the xenon
headlights level (Neumann, 2006).
When examining the actual trend within the technology and design
perspective, it is very important to highlight the next generation of adaptive front
lighting system (AFS), which features sequential LED lighting. The fashion in which
the optics in this system has been designed to function together has not yet been
seen or appreciated in the exterior lighting world. In addition to being a beautifully
designed optical arrangement, the system also helps drivers take corners and
curves more safely and consumes less energy while doing so due to the LED
usage (Sivak et al., 2005).
The system senses when the vehicle is approaching a curve and directs the
row of LEDs to switch on sequentially. As the vehicle turns, the LEDs illuminate at
a rate and intensity determined by the degree and speed of the turn. Electronic
sensors analyze inputs from the steering wheel and the vehicle speed to determine
how and when to illuminate the LEDs. The LEDs automatically switch off when the
51
road straightens out, but the main beam continues to illuminate the overall road.
The system functionality is shown of Figure 3.1. The total amount of luminous flux
is linearly correlated with the total current established by the fuzzy model. Each
increment of 350 mA on the current lights totally one LED and a sequential pattern
from one to five LEDs is followed comprising the LED array proposed. Regarding
the algorithm, the TSK model was adopted and two variables are considered in the
antecedents: the curve radius and the vehicle speed. The output of the fuzzy
inference model establishes the light intensity (total electrical current) to be added
to the array of LEDs. Assuming that the light intensity is distributed progressively
through the LEDs, the definition of which LED will be lit is directly related to the
total current value calculated by the model.
Le
d
5
400
Le
d
4
320
Le
d
3
240
Le
d
2
160
Speed
Fuzzy model
Total
electrical
current
Le
d
1
80
350
700
Radius of
the curve
1050
1400
1750
Total
electrical
current (mA)
Figure 3.1: System block definition.
52
The Birdseye view is a great tool to highlight the improvement that can be
obtained with the AFS. It shows the light distribution (width, length and intensity) on
the road from the top view. On Figure 3.2, the light distribution is shown with its
aperture and range. This is an existing headlamp low beam distribution with the
incremental of light shown in yellow. The range is almost 150 meters and the
aperture is 40 meters. Also the light intensity varies from red (more intense) to blue
(less intense) and the homogeneity of this light distribution can be perceived
through the color shift from red to blue. The result with AFS is more light,
accurately placed, and, more importantly, light is being added (Figure 3.2) for more
visibility during night time driving, not taken from one spot on the road and having it
moved to another, as today's cornering systems do (Hara et al., 2001).
Figure 3.2: Birdseye view and the improvement obtained with the AFS: the light
(yellow) is added to the beam pattern.
The concept that will be investigated in this study incorporates five LEDs
which will be lit according to the fuzzy model incorporated in the device.
53
3.4
Methodology
According to the previous section, the strategy adopted was to measure two
signals, namely, the road curvature and vehicle speed, measured directly from the
vehicle to determine the lighting level of each LED. For a vehicle with AFS, there is
a strong relationship between these inputs and the luminous flux ratio (Hogrefe,
2000; Ishiguro and Yamada, 2004). On Table 3.3 one can find the measured data
where there is a relation between these input variables with the luminous flux ratio.
It shows that there is a significant interaction between low speed and wide
curvatures. This data was acquired from a series of vehicle level evaluations at the
Ford test track in Dearborn, Michigan. Particularly in these conditions, there is an
improvement in the luminous flux ratio obtained in a vehicle when the comparison
is made between the system with AFS and without AFS.
Table 3.3: Improved illumination by AFS
Road scenario and speed conditions
Speed (km/h)
Road curvature
Luminous flux ratio
Without AFS
With AFS
radius (m)
40
50
1
3.9
70
150
1
2.8
90
280
1
1.6
54
This study will analyze the impact of the implementation of fuzzy type 1 or 2
to control the AFS and compare with the specialist criteria. There are several
methods to investigate the luminance and measurements on the road. For
example, the quantity and the quality of road lighting can be analyzed and
controlled with road surface luminance measurements. As a reference, the
European standard for road lighting calculations EN 13201-3 (EN, 2003) describes
methods for luminance calculations and measurements (Ekriasa et al., 2008).
The evaluation of the fuzzy inference system was accomplished through the
comparison between its prediction and the specialist response, using the
measurements raised on the test track presented in Figure 3.3 (São Paulo, Brazil).
This track reproduces several different driving conditions, varying speed and
curves simulating the highway and city driving conditions. This test track
represents the road condition and driver’s behavior. This data acquisition was
possible due to the VBOX III device installed inside the vehicle. It features a
powerful engine capable of providing 100 Hz update rate of all positioning
parameters including velocity, heading and position (latitude and longitude).
Velocity and heading data are calculated from Doppler shift in the positioning
carrier signal to provide high accuracy. It allows the acquisition of fifty
measurements per second, with each measurement comprising both speed and
position (longitude and latitude). This approach enables precise information about
the driver’s behavior before, during and after the curve entrance. The data was
acquired from the test track during a 4 period lap, which represents approximately
1,000 seconds.
55
Figure 3.3: Test track.
The fuzzy inference system comprises the vehicle’s speed (km/h) and
curvature radius (m) as inputs and the output is the electric current applied to drive
the array of LEDs. The universes of discourse adopted for the speed and the
curvature radius were [0; 1000] and [0; 140], respectively, which incorporates all
possible environment and velocity. In the simulation, a comprehensible
representation of the system structure is adopted to investigate the model
performance. Each input variable (linguistic variable) is featured by three fuzzy sets
that represent three meanings or linguistic terms: small, medium and high. All the
membership functions considered to represent these fuzzy sets are showed in
Figure 4 and were proposed by the specialist. This is an initial approach that can
be updated and increased or reduced if necessary, after the data analysis. Also,
this model, although developed with data from a test track, is suitable for all road
and environment circumstances, such as, freeways, highways and city traffic, to
mention a few.
56
(a)
(b)
Figure 3.4: membership functions related to the radius (a) and to the speed (b).
This work comprises the development and test of some models, structured
according to type-1 and type-2 fuzzy logic, based on human specialist knowledge.
This knowledge base is presented in Table 3.4 that shows a value for the electrical
current to the LEDs, according to the specialist, for each possible combination
between the input ranges also defined by the specialist. The combination between
high speed (speed > 90 km/h) and small radius (radius < 50 m) was not considered
due to the fact that this situation would imply in the crash of the vehicle. Figure 3.5
57
presents the output (current to LED), according to Table 3.4, for each input-output
pair collected and showed in the Figure 3.4.
The rules of Table 3.4 comprise a model that presents discontinuity in the
output (current value) and does not have a parallel processing (only one rule will
be active and will be considered for each set of values of speed and radius). The
rules and values suggested by the specialist represent an effort to identify or
quantify the ideal visual comfort for each situation.
Figure 3.5: Surface generated from Table 4.4 data.
The modeling of this problem using a fuzzy structure enables the
obtainment of a model that provides continuous values for the electrical current
and is also capable to consider the uncertainty associated to the definition of the
membership grade of radius and speed values with relation to each range (set)
defined by the specialist.
58
Table 4.4: Antecedents combination and desired output (specialist)
# Rules for the antecedents
Current to LEDs (mA)
(specialist)
1 If speed < 40 km/h and radius < 50 m
1,750
2 If speed < 40 km/h and 50 m < radius < 280 m
1,400
3 If speed < 40 km/h and radius > 280 m
1,050
4 If 40 km/h < speed < 90 km/h and radius < 50 m
1,350
5 If 40 km/h < speed < 90 km/h and 50 m < radius < 280 m
1,000
6 If 40 km/h < speed < 90 km/h and radius > 280 m
700
7 If speed > 90 km/h and radius > 280 m
350
8 If speed > 90 km/h and 50 m < radius < 280 m
0
Figure 3.6: Current values according to the specialist.
59
All the model structures considered in this work were based on the TSK
inference model in such a way that the electric current value in each rule is related
directly with the input values through a linear parametric model. In order to adjust
the fuzzy model performance, some parameters presented in the rule consequents
were estimated through the solution of an optimization problem that minimizes the
deviations between the model predictions and the desired current value defined by
the specialist. This parameter estimation procedure keeps the intrinsic features of
the fuzzy models tested and enables that these models can be adherent or close to
the specialist knowledge. The type-2 TSK model proposed in this work comprised
type-1 fuzzy sets in the antecedents according to the membership functions
presented in the Figure 3.6 and type-1 fuzzy sets were adopted to represent some
consequent parameters. Two aspects supported the choice of this approach in the
type-2 fuzzy modeling. First, the membership functions of radius and speed were
defined by only one specialist according to the Figure 3.4 and uncertainties about
these sets are not meaningful to justify the adoption of type-2 fuzzy sets in the
antecedents (inputs). Second, possible uncertainties in the model definition are
concentrated in the output of each rule and type-1 fuzzy sets were adopted in the
consequent parameters in order to represent this uncertainty inherent to the
definition of the light intensity (total electrical current) in each rule. The MATLAB
program developed is available in Appendix B.
60
3.5
Results and discussion
The simulation tests comprised the performance analysis of five fuzzy model
structures that differ among themselves only with relation to the consequent model
considered. Table 3.5 presents the output models tested the sum of squared errors
(SSE) between the model results and the specialist responses in all data set, the
standard deviation, the percentage of average standard deviation error and
percentage of the average quadratic error for each case. Model 1 is a zero order
TSK model of type-1 that not considers the direct effect of the velocity and radius in
the consequent value. Model 2 is type-1 first order linear model that considers the
velocity as input variable. Model 3 is similar to model 2 except for the fact that the
radius was adopted as input. An additional test comprised the inclusion of a hard
constraint in the parameter estimation problem, in order to avoid model predictions
below 10% (model 4) of the specialist decision. This constraint was proposed to
ensure that the simulation results would not change more than 10% from the
specialist assessment, even if the sum of squared errors could increase. This
approach imposes that the fuzzy model must guarantee the amount of light on the
road.
Despite the type-2 fuzzy model structure adopted in this work, the use of
interval type-1 sets to represent the consequent parameters in a type-2 TSK model
(with type-1 sets in the antecedents) would not be a suitable choice since it
provides the same output of a type-1 TSK model whose consequent parameters
are the centers of the consequent sets of the interval type-2 TSK model (Mendel,
2001). In order to obtain a model better than all type-1 structures to control the
hardware proposed (LEDs AFS), the type-2 fuzzy model considered in this work
61
comprised the inclusion of an additional uncertainty level in the consequent of each
rule (model output) through the adoption of parameters represented Gaussian
(represented by a0 ± σ ) (model 5) type-1 fuzzy sets. In this case, the mean and
deviation of sets in all rules were considered in the model adjustment procedure.
However, the results presented in Table 3.4 show that in this case the type-2
structure proposed did not provided meaningful improvements in the model
performance compared with the type-1 structure (model 2) and the model 5 was
rejected as alternative to the LED control. This decision is also based on the higher
simplicity of model 2 (type-1) in comparison with model 4 (type-2), mainly featured
by a small number of parameters (deviations of type-1 Gaussian fuzzy sets in the
consequents are not considered).
All fuzzy models listed in Table 3.5 comprise 8 rules (Table 3.4) and the
output model parameters have specific values for each rule. For example, the
number of parameters to be estimated in the model 1 will be, at the most, equal to
8, and the specialist values presented in Table 3.4 were used as initial guess in the
parameter estimation algorithm. In all cases the intersection operation (type-1
fuzzy models) was performed applying the product operator (Mendel, 2001). The
operations used in the type-2 fuzzy model (meet and generalized centroid) were
performed according to the extension principle (Karnik and Mendel, 2000).
62
Table 3.5: Output models adopted and results
Model
Fuzzy
Output model
Type
% Avg.
Quadratic
Std
% Avg.
Quad Error
Error
Deviation
Std Deviation
1
1
y = a0
34.76
6,975.80
437.65
86.18
2
1
y = a0 + a1 ⋅ V
25.18
5,054.43
317.07
62.44
3
1
y = a0 + a1 ⋅ R
34.01
6,826.66
427.63
84.21
4 (*)
1
y = a0 + a1 ⋅ V
27.23
5,061.11
322.33
65.14
5
2
y = (a0 ± σ ) + a1 ⋅ V
25.18
5,054.42
317.07
62.44
(*) - Parameter estimation problem with hard constraint.
Figure 3.7 shows the best result of type-1 fuzzy models, with the lowest
squared error (model 2). Although model 2 presents better results among all
others, it still presents a high degree of variability which can be considered high
and could lead some kind of discomfort to the driver. In addition, another aspect to
be mentioned is the light output. Despite the fact that the model presents a good
overall performance, in some cases it suggests current values below the specialist
recommendation. This could result in more one discomfort element for the driver
also considering the transport security. This feature suggests an opportunity to
improvement in order to allow the driver to have more light on the road. One
alternative to cope with these is to consider the adoption of hard constraints in the
parameters estimation procedure. This approach was effectively implemented
through model 4 that incorporates a hard constraint in which the values of current
output should not vary more than 10% from the specialist’s recommendation. The
surface highlights the circumstances on which the LEDs will operate. Confirming
63
the coherence of the model, Figure 3.7(b) presents a ramp showing that the
current increases when the radius and speed decrease. The existence of a non
uniform surface, with some abrupt drops in the current is expected and is
associated to the nonlinearity of the fuzzy model and its intrinsic ability to
interpolate different operational ranges.
Figure 3.7: Comparison between specialist and model 2 (a) and surface generated
from model 2 (b).
Figure 3.8 shows the results obtained with model 4 (constraint of 10% in the
parameters estimation procedure with fuzzy type-1). In this model there is more
64
light in the road due to the fact that the restriction give a light distribution with more
comfort to the customer. Lower constraints were also tested, but a 10% restriction
showed a better performance due to the fact that it provided reduced fluctuations
(small variance) and ensured the best outcome within the smaller quadratic error.
Considering the global performance, model 4 would provide the better
behavior according to the best expertise knowledge. In order to confirm that model
4 is also coherent according to its fuzzy surface, Figure 3.8(b) shows this surface
which presents the increase of current when the curves and speed decrease and
the decrease of current when the speed and radius increase. Also it shows a
smoother surface when compared to model 2, specially around 60 m of radius
where model 2 has an abrupt rupture generated by the fluctuation of the model.
Figure 3.8(b) shows model surface which presents the increase of
current when the curves and speed decrease and the decrease of current when
the speed and radius increase. Also it shows a smoother surface when compared
to model 2, specially around 60 m of radius where model 2 has an abrupt rupture
generated by the fluctuation of the model. In this specific area, around 60 m, it is
very important to have a smooth distribution due to the fact that visual perception is
noticeable on curvature corners helping the driver at night.
65
(a)
(b)
Figure 3.8: Comparison between specialist and model 4 (a) and surface generated
from model 4 (b).
Both models 2 and 4 have a better surface accommodation when compared
to the surface generated from Table 3.4 (Figure 3.5) which is not only more abrupt
but has a lot of steps and imperfections on its surface. The surfaces of Figures 3.7
and 3.8 comprise a more continuous behavior than the surface on Figure 3.5
(specialist surface) and also present a homogenous feature that contributes to
reduce the risk of possible distractions (or mistakes) of the truck driver during
66
nighttime driving. The comparison between Figures 3.7-3.8 and 3.5 is enough to
justify the adoption of a modeling procedure, suitable for the reality described,
instead of the specialist rules itself.
3.6
Conclusions
This chapter describes the development and application of a fuzzy rulebased system, for modeling and predicting the amount of light to be added to the
driver during night time driving. The proposed modeling framework comprised a
suitable combination between expert’s knowledge and a parameter estimation
procedure that enabled the adjustment of the output models of each rule,
considering different type-1 and type-2 fuzzy structures. More specifically, a
specialist’s engineering judgment could be appropriately incorporated in the fuzzy
rule base according to the knowledge and experience on night time and
environmental conditions.
Figure 3.8(b) shows model surface which presents the increase of current
when the curves and speed decrease and the decrease of current when the speed
and radius increase. Also it shows a smoother surface when compared to model 2,
specially around 60 m of radius where model 2 has an abrupt rupture generated by
the fluctuation of the model. In this specific area, around 60 m, it is very important
to have a smooth distribution due to the fact that visual perception is noticeable on
curvature corners helping the driver at night.
Some of the models are quite adequate, but the best alternative would be
model 4, which considers the restriction that guarantees the specialist knowledge
67
in the best way. These models have output responses similar to the specialist,
highlighting and reinstating the importance of making nighttime driving safer, while
providing more light to the driver as well. Although the solutions do not give the
minimum quadratic error, they allow the driver to see more at night due to the fact
that they generate almost the same current output as the specialist’s model. The
idea is that the specialist understands the customer needs due to his knowledge.
Finally, it is important to highlight that the use of LEDs is due to certain
advantages versus halogen lighting, not only regarding long life, power
consumption and color temperature (to mention a few), but also regarding design
trends around this concept. Further investigation will be sought to improve the
system and its algorithms. Moreover, it shows that new technologies can improve
not only the appearance but also the overall performance of the lighting system.
Nomenclature
a0, a1
output model parameters;
∆
deviation of type-1 interval fuzzy set;
σ
standard deviation of type-1 Gaussian fuzzy set;
y
current to LEDs (mA);
V
velocity of the vehicle velocity (km/h);
R
radius of the curve (m).
HID
high-intensity discharge
68
References
AL, 2009. Automotive Lighting website. http://www.al-lighting.de.
Altinten, A., Erdŏgan, S., Hapŏglu, H., Alpbaz, M., 2003. Control of a
polymerization reactor by fuzzy control method with genetic algorithm, Comput.
Chem. Eng. 27, 1031–1040.
Anderson, T., Sawyers, T., Bell, M, 1998. Optimization of a Fuzzy Logic Traffic
Signal controller by a multiobjective genetic algorithm, Transport Research
Laboratory, 186-190.
Babuška, R., Verbruggen, H. B., 1996. An overview of fuzzy modeling for control,
Control Eng. Practice 4, 1593–1606.
Bellman, R. E., Zadeh, L. A., 1970. Decision-making in a fuzzy environment,
Management Science 17, 141–164.
Chen, B., Liu, X., 2004. Reliable control design of fuzzy dynamic systems with
time-varying delay, Fuzzy Sets and Systems 146, 349–374.
Du, G.-N. Zhu, Z.-Y., 2006. Modeling Spatial Vagueness Based on Type-2 Fuzzy
Set, Journal of Zhejiang University SCIENCE A 1, 250-256.
Dubois, D., Prade, H., 1978. Operations on Fuzzy Numbers, Int. J. Systems
Science 9, 613-626.
Dubois, D., Prade, H., 1979. Operations in a Fuzzy-Valued Logic, Information and
Control 43, 224-240.
Ekriasa, A., Eloholma, M., Halonen, L., Song, X., Zhang, X., Wen, Y., 2008. Road
lighting and headlights: Luminance measurements and automobile lighting
simulations, Building and Environment 43, 530–536.
69
EN, 2003. Road lighting—Part 3: Calculation of performance, European standard
EN 13201-3, Publication 270-2003, Ref. No. EN 13201-3: 2003 E.
Gotoh, S., Aoki, T., 1996. Development of active headlight, 15th International
Conference on the Enhanced Safety of Vehicles, Melbourne, Australia.
Hagras, H. A., 2004. A Hierarchical Type-2 Fuzzy Logic Control Architecture for
Autonomous Mobile Robots, IEEE Transactions on Fuzzy Systems 12, 524-539.
Hamm, M., 2002. Adaptive lighting functions history and future-Performance
investigations and field test of user acceptance, SAE Technical Paper Series,
No. 2002-01-0526.
Hara, T., Kuramochi, T., Ayama, M., Kojima, S., Sato, T., 2001. Evaluation of AFS
from driver’s point of view, Progress in Automobile Lighting, Darmstadt,
Germany’ Technical University of Darmstadt, 397-402.
Hisdal, E., 1981. The IF-THEN ELSE Statement and Interval-Values Fuzzy Sets of
Higher Type, Int’l J. Man-Machine Studies 15, 385-455.
Hogrefe, H., 2000. Adaptive front lighting systems for optimum illumination of
curved roads, highway lanes and other driving situations, SAE Technical Paper
Series, No. 2000-01-0431.
Ishiguro, K., Yamada, Y., 2004. Control technology for bending mode AFS, SAE
Technical Paper Series, No. 2004-01-0441.
Just Auto, 2007. Annual just-auto’s review of 2007 - Management briefing, Just
Auto, Aroq Limited, UK.
Karnik, N. N., Mendel, J. M., 1998. Introduction to Type-2 Fuzzy Logic Systems,
Fuzzy Systems Proceedings, IEEE World Congress on Computational
Intelligence, The 1998 IEEE International Conference 2, 915-920.
70
Karnik, N. N., Mendel, J. M., 2000. Operations on Type-2 Fuzzy Sets, Int’l. J. on
Fuzzy Sets and Systems 1, 327–348.
King, P. J., Mamdani, E. H., 1977. The application of fuzzy control systems to
industrial processes, Automatica 13, 235–242.
Kittel, C., 2004. Introduction to Solid State Physics, 8th Edition, Wiley, USA.
Kosonen, L., 2003. Multi-agent fuzzy signal control based on real-time simulation.
World Congress on Intelligent Transport System, October, Transportation
Research Part C: Emerging Technologies 11, 389-403.
Hella, 2009. Hella website. www.hella.de.
Levinshtein, M., Rumyantsev, S., Shur, M., 2001. Properties of Advanced
Semiconductor Materials: GaN, AIN, InN, BN, SiC, SiGe, Wiley-Interscience,
USA.
Li, L., Lin, W.-H., Liu, H., 2006. Type-2 Fuzzy Logic Approach for Short-term Traffic
Forecasting, Intelligent Transport Systems, IEEE Proceedings 153, 33-40.
Liang, Q., Karnik, N. N., Mendel, J. M., 2000. Connection Admission Control in
ATM Networks Using Survey-based Type-2 Fuzzy Logic Systems, IEEE
Transactions on Systems, Man and Cybernetics, Part C 30, 329-339.
Liang, Q., Mendel, J. M., 2000. Interval Type-2 Fuzzy Logic Systems: Theory and
Design, IEEE Transactions on Fuzzy Systems 8, 535-550.
Mendel, J. M., 2001. Uncertain Rule-based Fuzzy Logic Systems: Introduction and
New Directions, Prentice-Hall P. T. R., London, UK.
Mendel, J. M., 2003. Fuzzy Sets for Words: a New Beginning, The IEEE
International Conference on Fuzzy Systems, 37-42.
71
Mendoza, O., Licea, G., Melin, P., 2007. Modular Neural Networks and Type-2
Fuzzy Logic for Face Recognition, Annual Meeting of the North American Fuzzy
Information Processing Society, NAFIPS '07, 622-627.
Mizumoto, M., Tanaka, K., 1976. Some Properties of Fuzzy Sets of Type-2,
Information and Control 31, 312-340.
Mizumoto, M., Tanaka, K., 1981. Fuzzy Sets of Type-2 Under Algebraic Product
and Algebraic Sum, Fuzzy Sets and Systems 5, 277-290.
Nakamura, S., 1996. High-brightness blue/green LEDs and first III-V nitride-based
laser diodes, Proc. SPIE Physics and Simulation of Optoelectronic Devices
2693, 43-56.
Neumann, R., 2004. AFS halogen headlamp system: Experimental study and first
field results, SAE Technical Paper Series, No. 2004-01-0439.
Neumann, R., 2006. LED Front Lighting – Optical Concepts, Styling Opportunities
and Consumer Expectations, SAE Technical Paper Series, No. 2006-01-0100.
Niittymäki, J., Turunen, E., 2003. Traffic signal control n similarity logic reasoning.
Fuzzy Sets and Systems 133, 109-131.
∅stergaard, J.-J., 1977. Fuzzy logic control of heat exchange process, in: M. M.
Gupta, G. N. Saridis, B. R. Gaines (Eds.), Fuzzy Automata and Decision
Process, North-Holland, Amsterdam, 285-320.
Pareek, U., Kar, I. N., 2006. Estimating Compressor Discharge Pressure of Gas
Turbine Power Plant Using Type-2 Fuzzy Logic Systems, IEEE International
Conference on Fuzzy Systems, 649-654.
72
Ren, Q., Qin, Z., Baron, L., Birglen, L., Balazinski, M., 2007. Identification of RigidBody Dynamics of Robotic Manipulators Using Type-2 Fuzzy Logic Filter,
Annual Meeting of the North American Fuzzy Information Processing Society,
NAFIPS '07, 387-392.
Sala, A., Guerra, T. M., Babuška, R., 2005. Perspectives of fuzzy systems and
control, Fuzzy Sets and Systems 156, 432-444.
Schmocker, J.; Ahuja, S.; Bell, M., 2008. Multi-objective signal control of urban
junctions – Framework and a London case study, Transportation Research Part
C 16, 454-470.
Sivak, M., Schoettle, B., Flannagan, M., Minoda, T., 2005. Optimal strategies for
adaptive curve lighting, Journal of Safety Research 36, 281-288.
Stern, D., 2008. Global market review of automotive lighting –forecasts to 2013,
2008 edition, Just Auto, Aroq Limited, UK.
Tahayori, H., Tettamanzi, A. G. B., Degli Antoni, G., 2006. Approximated Type-2
Fuzzy Set Operations, IEEE International Conference on Fuzzy Systems, 19101917.
Thovutikul, S., Auephanwiriyakul, S., Theera-Umpon, N., 2007. Microcalcification
Detection in Mammograms Using Interval Type-2 Fuzzy Logic System, IEEE
International Fuzzy Systems Conference, FUZZ-IEEE 2007, 1-5.
UNECE,
2008.
UNECE
website.
http://www.unece.org/trans/main/wp29/
wp29regs41-60.html
Wördenweber, B., Wallaschek, J., Boyce, P., Donald D. Hoffman, D., 2007.
Automotive Lighting and Human Vision, Springer Verlag, USA.
73
Zadeh, L. A., 1975. The Concept of A Linguistic Variable and Its Application to
Approximate Reasoning – 1, Information Sciences 8, 199-249.
Zukauskas, A., Shur, M., Caska, R., 2002. Introduction to Solid-State Lighting,
Wiley Interscience, USA.
74
Capítulo 4
Desenvolvimento de um modelo AFS (sistema
adaptativo de iluminação veicular) para assegurar as
necessidades do motorista de caminhão durante o
período noturno e vislumbrando sua utilização para o
mercado Brasileiro
Apresentação
No capítulo anterior foram desenvolvidos e analisados modelos
baseados em lógica fuzzy para o controle de um sistema adaptativo de
iluminação veicular dianteira a LEDs para carros. O conjunto de regras, as
condições de contorno (pista de testes), o conhecimento do especialista, a
estrutura de modelo fuzzy adotada, as variáveis de entrada e as respectivas
funções de pertinência foram apresentados e discutidos juntamente com os
resultados, evidenciando-se o desempenho dos modelos desenvolvidos no
controle da intensidade de iluminação dos LEDs de acordo com a velocidade
do veículo e o raio de curvatura da rodovia. Este capítulo tem por objetivo
definir qual o modelo fuzzy mais adequado para assegurar as necessidades do
motorista de caminhão levando em consideração as condições das estradas,
da pista de testes e o meio ambiente do mercado brasileiro, bem como uma
pesquisa específica para entender as necessidades e comparar as diferenças
entre os motoristas de carro e caminhão. Portanto, este capítulo representa
uma continuação lógica do trabalho desenvolvido no capítulo anterior. Este
último, conforme explicitado, foi importante para consolidar uma metodologia
de modelagem para o problema, evidenciando a potencialidade das estruturas
testadas. Aqui, a partir das informações relacionadas ao caminhão, entre elas a
75
pesquisa de campo realizada, o levantamento das condições de pista e os
experimentos na pista de testes, foi detectada a necessidade de um modelo
específico para caminhões, e as estruturas de modelo foram então estendidas
para este caso. Os desenvolvimentos feitos neste capítulo e nos anteriores
fornecem a base conceitual e de modelagem para o desenvolvimento de um
protótipo para validar este modelo, havendo um grande potencial para que o
sistema desenvolvido se torne um item de série opcional da montadora ou um
acessório.
Resumo
O comportamento de dirigir à noite é diferente do comportamento diurno
devido aos diferentes cenários apresentados ao campo de visão do motorista.
À noite os motoristas têm de depender no sistema de iluminação dos veículos
para enxergar o meio ambiente e as condições das estradas à sua frente. Nas
décadas
recentes,
o
sistema
de
iluminação
tem
tido
expressivos
desenvolvimentos tecnológicos, como o uso de LEDs em sistemas adaptativos
de iluminação veicular (AFS), o que representa uma inovação para a indústria
de iluminação automotiva. Isto tem se tornado rapidamente uma das mais
inovadoras tecnologias no mundo dentro da comunidade de iluminação
veicular. Este capítulo discute as necessidades do motorista (carro e caminhão)
dado o meio ambiente e as estradas do estado de São Paulo, no Brasil,
levando em conta uma pesquisa comparativa entre as necessidades de ambos
motoristas, carro e caminhão, sob condições diferentes das ruas. A discussão
do controle do sistema de AFS com LEDs adotando lógica fuzzy 1 e 2, de
acordo com as necessidades do motorista de caminhão, também faz parte
76
deste capitulo, bem como a análise da importância da estratégia do modelo,
quando adotando decisões que são baseadas no conhecimento humano. Os
resultados demonstram uma potencial proposta de metodologia para controlar
os sistemas de iluminação.
Palavras-chave:
comportamento,
lógica
fuzzy,
AFS,
LED,
caminhão,
iluminação
Conclusão
Este capítulo descreve as necessidades e comportamento de ambos os
motoristas, de carro e caminhão, levando em consideração a iluminação das
ruas de São Paulo, no Brasil. O estudo gerou uma vasta quantidade de dados
que podem suportar o desenvolvimento de um sistema específico de AFS a
LEDs, levando em conta não só os dados da pesquisa, mas também as
condições das estradas e dos resultados das simulações feitas no MATLAB.
As informações dadas ao especialista ajudaram a definir a intensidade e
a distribuição de luz requerida quando o sistema AFS estiver disponível. É de
vital importância destacar a necessidade de um modelo específico para o
motorista de caminhão. O modelo proposto confirma que o modelo de carro
não pode ser adotado pelo motorista de caminhão, pois não atende as suas
necessidades. Este estudo deixa claro que as necessidades dos motoristas de
caminhão e de carro são diferentes, não somente pelo campo de visão e pelo
tempo de resposta, mas também pelos anseios em relação à iluminação
veicular, que são diferentes, conforme destacado na seção 4.3. Até o presente
momento, não existem estudos neste campo e este capítulo mostra que
77
existem muitos aspectos que devem ser estudados posteriormente. Além disso,
os dados coletados neste estudo permitem o desenvolvimento de um sistema
específico para o Mercado brasileiro para os faróis baixo e alto, para ambos
carros e caminhões.
O modelo de AFS a LEDs para caminhões (conjunto de LEDs com ótica,
sistema
de
alimentação
elétrica,
micro-controlador
com
lógica
fuzzy
embarcada) para melhorar a dirigibilidade noturna foi desenvolvido levando em
consideração o conhecimento do especialista e a estimação dos parâmetros
que permitiu o ajuste do modelo de saída de cada regra, levando em
consideração diferentes estruturas fuzzy tipo 1 e tipo 2. Mais especificamente,
o julgamento do especialista pode ser incorporado na formação das regras
fuzzy de acordo com as condições das estradas e do meio ambiente. É
necessário enfatizar que a solução para o segmento de caminhões foi a
número 1, que demonstra uma superfície com menor variabilidade e utiliza
fuzzy do tipo 1, o que simplifica o modelo de saída. O modelo escolhido
confirma o conhecimento do especialista e claramente reduz a complexidade
do mecanismo global de controle para esta aplicação.
Finalmente, são necessários estudos adicionais neste campo de
pesquisa, mais especificamente para desenvolver um sistema de AFS a LEDs
para ser incorporado no segmento de caminhões, como um acessório ou um
item opcional da montadora. A patente para este modelo proposto está sendo
requisitada, devido à sua inovação no mercado brasileiro. Esta seria a primeira
vez de aplicação na indústria automotiva e iria gerar um grande avanço
tecnológico no segmento de caminhões.
78
Chapter 4
Design of a fuzzy-based AFS (Advanced Front Lighting
System) model to meet truck driver’s needs during
nighttime driving: foreseeing its use in the Brazilian
market
Opening statement
In the previous chapter, models were developed and analyzed adopting
fuzzy logic to control an adaptive front lighting system with LEDs for cars. The
set of rules, surroundings (test track) specialist expertise, fuzzy models
adopted, input variables and the pertinence functions were presented and
discussed together with the results, highlighting the models performance
developed to control the current LED output, according to the speed and road
curvature.
This chapter main goal is not only to determine which is the most
adequate fuzzy model to ensure the truck driver’s needs taking into
consideration the roads, test track, environment of the Brazilian market, but also
a detailed survey to understand the driver’s needs and compare the differences
between the truck and car driver. So, this chapter represents the logic
continuation of the work developed in the previous chapter. The last one, as
highlighted, was very important to consolidate the methodology to face the
problem, confirming the outstanding results of the tested structures. In fact, after
the summarization of the truck related data, among them, the field survey, the
road conditions and the tests performed at the test track, the real need for a
specific algorithm for the truck driver’s need was confirmed, and the related
structure were extended to the truck model.
79
The development that was done in this chapter and in the previous ones,
where the conceptual basis to develop a bench prototype and validate this
model, there being a huge potential for the development of the component,
either as an accessory or an end item at the assembly line at the OEM (Original
Equipment Manufacturer).
Abstract
Nighttime driving behavior differs from that during the day because of
unique scenarios presented in a driver's field of vision. At night drivers have to
rely on their vehicle headlamps to illuminate the road as it is essential that the
driver is able to see the environment and road conditions in front of him. In
recent decades the vehicular illumination system has undergone expressive
technological advances such as the use of a Light Emitting Diode (LED)
Adaptive
Front-lighting Systems
(AFS),
which
represents
an industry
breakthrough in lighting technology. This is rapidly becoming one of the most
important innovative technologies around the world within the lighting
community. This paper discusses driver’s (car and truck) needs given the
environment and road conditions in the state of São Paulo (Brazil) using a
survey applied to compare the needs of both truck and car drivers under
different road conditions. A discussion of LED AFS control alternatives using
fuzzy logic according to truck drivers’ needs is also part of this paper, as is the
analysis of the importance of such a modeling strategy, when decisions are
based on human knowledge. The results show the potential and suitability of
the methodology proposed for controlling truck-related lighting.
Keywords: behavior, fuzzy logic, AFS, LED, truck, lighting
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4.1
Introduction
In nighttime driving it is important to enhance safety for truck drivers due
to the fact that the time of response of a truck and its driver is much longer
when compared to that of a car and its driver. There have been a series of
papers describing the differences between the behavior of drivers at nighttime
when comparing gender, eye movement, road signs and age factors (Liu et al.,
1998; Gish, 2001; Burg, 1967; Egberink et al., 1996; Okombi-Diba et al., 2007).
However, up until now, truck drivers have not been considered candidates for
this kind of comparison, even though their driving needs are clearly as important
(if not more so) as those of a car driver.
The primary aim of innovative headlamp systems is to increase traffic
safety especially at night. In order to achieve this, optimum light distribution
depending on driving and environmental conditions must be available to better
illuminate the road and its surroundings. Brightness conditions, weather, road
conditions, traffic conditions, type of road, vehicle speed and acceleration
behavior must all be taken into account by an Adaptive Front-lighting System
(AFS) (Neumann, 2004). The LED AFS model proposed here consists of a LED
array with optics, electronic power driving system, onboard micro-controller and
fuzzy logic built-in software controller for which a patent is being requested for
use in the Brazilian market. Lukacs et al. (2009) developed AFS control
alternatives using fuzzy logic models (types 1 and 2) taking into consideration
both driver and road conditions in the state of São Paulo (Brazil). These models
were developed and applied to a LED (Light Emitting Diode) control algorithm
and the rules were defined according to specialist knowledge (section 4.4). The
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survey data presented in this paper together with the data collected at the test
track (section 4.4) identified the need to develop specific models for the LED
AFS control in trucks, adjusted for each target customer.
A survey was applied to a population of 200 experienced car and truck
drivers in order to understand the needs of truck and car drivers, the road
conditions and environment (highlighted in section 4.2). This survey is
discussed in section 4.3, and the data collected also generated key elements
for future developments regarding not only AFS configuration, but also the
entire beam pattern distribution for low and hi beams applications. Section 4.4
describes the test track measurements and in section 4.5 results are presented
and discussed. This last section presents the structure of fuzzy models
proposed and its features (i.e. membership function parameters associated to
the consequents of each rule), an estimation procedure that comprises the
comparison between specialist responses (desired responses) and ideal model
results using data related to the truck driver’s needs and road conditions of the
Brazilian target customer (Wördenweber et al., 2007). This parameter
estimation procedure enabled the selection of parameter values that could
provide optimized solutions, considering that expert knowledge may not always
involve practical rule-of-thumb guidelines concerning the appropriate estimation
of these parameters. In section 4.6, concluding remarks are presented.
4.2
Road conditions and environment
This study focuses on the road conditions in São Paulo, Brazil, because
it has the largest vehicular fleet in the country. This state has a total of 34,600
km of paved highways of which 22,000 km are state-operated roads, 1,050 km
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are federal-operated roads and 11,600 km are county roads. According to
Annual Research (Fenabrave, 2008) from the Brazilian National Transport
Confederation (CNT), São Paulo has the best road scenario in the country as
regards overall illumination, surfacing and signs. In 2004, 59.4% of the
highways were classified as excellent by CNT standards. DER (Brazilian Road
Department) has 28 web linked cameras available 24 hours a day with direct
information on road conditions. In Figure 4.1 there is a map displaying the roads
in the state of São Paulo (SP).
Figure 4.1: Map displaying roads in SP.
In Figures 4.2 and 4.3 some examples and a comparison between night
and daytime scenarios can be found. It is important to compare these in order to
properly understand the complete analysis. Figure 4.2a shows a 180 degree
angle curvature which may cause accidents due to the negative angle. Through
comparison with the night scenario in Figure 4.2b, this light distribution does not
83
follow the road and might cause glare to the oncoming traffic in the other
direction. When heavy truck usage is brought into this scenario, it becomes
even worse given their time response. Figure 4.3a illustrates a rescue / road
assistance area during the daytime with a truck. Figure 4.3b shows the same
environment at night without any signs, a situation which may lead to an
accident during the maneuvering, the entry or the exit of a vehicle.
(a)
(b)
Figure 4.2: (a) Daytime and (b) nighttime scenarios on SP99 Km 72.
(a)
(b)
Figure 4.3: (a) Daytime and (b) nighttime scenarios on SP99 Km 34.
These are some examples of the environment that characterizes the
highways in São Paulo: Figures 4.4, 4.5 and 4.6 are actual shots of these
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scenarios. It is of utmost importance to highlight that there are pedestrians and
cyclists on the sides of these highways (Figure 4.4) creating numerous potential
accidents due to the lack of signs or proper illumination. In Figure 4.5a, there is
an example of a lack of traffic signs on the crossroads and zero artificial
illumination/retro reflective devices to indicate the possibility of incoming traffic
through the lanes and also zero illumination coming off or into the tunnels. This
might lead to visual discomfort for both truck and car drivers during day and
night time driving. Figure 4.5b describes the morning fog which can also
generate some discomfort and lack of seeing distance for the driver. In these
conditions, both car and truck drivers have stated the need to improve the
quality of their lighting system to enhance their field of vision during not only
early morning fog but also during night time driving. Furthermore, Figure 6a
describes the changes in the road scenarios on the SP99 highway. In a short
distance, the driver will shift from a bridge to a road lined with trees and a small
forest. This will lead to lack of visibility ahead reducing the driver’s time of
response. On the other hand, Figure 4.6b shows the sinuous contour of the
SP123 which might lead to glare from the oncoming traffic and also indicates a
lack of visibility ahead.
85
(a)
(b)
Figure 4.4: (a) Cyclist and (b) pedestrian in red on SP55 Km 211 and SP270 km
17 respectively.
(a)
(b)
Figure 4.5: (a) Lack of traffic signs on the crossroads on SP099 and (b) daytime
fog on SP55 Km 292.
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(a)
(b)
Figure 4.6: (a) Environmental conditions of the highways SP99 and (b) SP123
km 31.
In addition, there are important data from the highways showing road
curvature. These images were taken from MapLink program (MapLink, 2008).
Two examples (Figure 4.7, a and b) are taken from the Regis Bittencourt road
(BR-116) which is one of the most dangerous highways in Brazil. The road
curvature does not help, particularly due to the fact that there are mountains
and continuous curves in the vicinity increasing the highlight effect. Moreover,
there are several 90 degree curves which under certain weather conditions may
cause severe accidents.
(a)
(b)
Figure 4.7: Two aerial views (a) and (b) of BR116.
87
The circumstances presented in this section allow the specialist, who is
an expert in the automotive lighting field with more than 15 years of experience
developing lighting systems in the auto industry, to understand the environment
(city, motorway and rural) and road conditions such as pedestrians, fog and
rain, for example, in the region which is used in the car and truck driver models.
4.3
Data from the driver survey
In order to understand the needs of the truck and car drivers, a thorough
survey was developed by the authors and applied to a population of 200
experienced car and truck drivers in the state of São Paulo in 2008. It is crucial
to draw attention to the fact that during nighttime the number of accidents in the
state of São Paulo represents more than 47% of the total amount of accidents
according to Intervias (2000).
This survey data helped to understand the needs of both truck and car
drivers. Furthermore, it highlights important aspects such as low and high light
beam usage in different scenarios, average speed and incoming glare for both
car and truck drivers. The issues addressed in the survey and its results are
presented on Table 4.1. The main purpose of this questionnaire was to
understand the needs and behavior of the drivers when driving at night. Some
elements were worthy of discussion, such as:
low beam and high beam usage under different road conditions;
influence of noise factors, such as rain, fog, pedestrians, animals,
cyclists; also in different scenarios;
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driver perception of the beam pattern regarding the light distribution,
near side illumination, road visibility ahead and also the impact of
glare and average speed.
89
Table 4.1: Questionnaire to understand driver needs while driving at nighttime in
the Brazil and survey results
Questions
Categories
Unit
(car: c (%) / truck: t (%))
1 What is your overall front lighting perception?
TOWN
2
What is your low beam usage
under different road conditions?
MOTORWAY
RURAL
TOWN
3
What is your hi beam usage under
MOTORWAY
different road conditions?
RURAL
What is the degree of visibility do
4 you have ahead of you under
different road scenarios?
5
What is the visualization of a
crossroads ahead of you?
MOTORWAY
RURAL
MOTORWAY
RURAL
TOWN
What is your average speed while
6 driving on the roads of the
MOTORWAY
following types?
RURAL
What is the near side illumination that can be
7 observed on the left and right side from your
point of view?
TOWN
What is the beam pattern lighting
8 distribution in front of the vehicle
(field of view)?
MOTORWAY
RURAL
TOWN
What is your visualization of
9 pedestrians / bicycle / animals
under different road conditions?
MOTORWAY
RURAL
10
What is the discomforting impact of the glare
generated from oncoming traffic?
TOWN
11
What is the perception of driving
under severe rain conditions?
MOTORWAY
RURAL
TOWN
12
What is the perception of driving
under severe fog conditions?
MOTORWAY
RURAL
Good
(c: 65 / t: 30)
50
(c: 51 / t: 39)
50
(c: 29 / t: 19)
50
(c: 28 / t: 25)
10
(c: 60 / t: 48)
10
(c: 30 / t: 22)
10
(c: 10 / t: 19)
Low
(c: 10 / t: 11)
Low
(c: 20 / t: 18)
Small
(c: 26 / t: 14)
Small
(c: 27 / t: 19)
50
(c: 40 / t: 70)
50
(c: 00 / t: 00)
50
(c: 28 / t: 55)
Average
(c: 22 / t: 55)
70
(c: 37 / t: 48)
70
(c: 61 / t: 56)
70
(c: 52 / t: 43)
30
(c: 30 / t: 27)
30
(c: 50 / t: 39)
30
(c: 60 / t: 45)
Medium
(c: 55 / t: 57)
Medium
(c: 60 / t: 57)
Medium
(c: 53 / t: 52)
Medium
(c: 49 / t: 51)
70
(c: 50 / t: 23)
70
(c: 20 / t: 39)
70
(c: 62 / t: 40)
Poor
(c: 13 / t: 15)
90
(c: 12 / t: 13)
(% of time
90
(c: 10 / t: 25)
used)
90
(c: 20 / t: 32)
50
(c: 10 / t: 25)
(% of time
50
(c: 20 / t: 39)
used)
50
(c: 30 / t: 36)
High
(c: 35 / t: 32)
High
(c: 20 / t: 25)
High
(c: 21 / t: 34)
High
(c: 24 / t: 30)
80
(c: 10 / t: 07)
80
km/h
(c: 80 / t: 61)
80
(c: 10 / t: 05)
Small
Medium
High
(c: 35 / t: 25) (c: 45 / t: 45) (c: 20 / t: 30)
Small
(c: 40 / t: 27)
Small
(c: 50 / t: 25)
Small
(c: 45 / t: 32)
Small
(c: 40 / t: 23)
Small
(c: 43 / t: 34)
Small
(c: 44 / t: 38)
Small
(c: 20 / t: 16)
Acceptable
(c: 40 / t: 27)
Acceptable
(c: 29 / t: 14)
Acceptable
(c: 32 / t: 25)
Acceptable
(c: 25 / t: 16)
Acceptable
(c: 17 / t: 16)
Acceptable
(c: 15 / t: 11)
Medium
(c: 50 / t: 64)
Medium
(c: 30 / t: 48)
Medium
(c: 40 / t: 50)
Medium
(c: 38 / t: 61)
Medium
(c: 36 / t: 34)
Medium
(c: 46 / t: 48)
Medium
(c: 30 / t: 29)
Poor
(c: 40 / t: 48)
Poor
(c: 60 / t: 50)
Poor
(c: 50 / t: 38)
Poor
(c: 35 / t: 50)
Poor
(c: 24 / t: 23)
Poor
(c: 35 / t: 34)
-
High
(c: 10 / t: 09)
High
(c: 20 / t: 27)
High
(c: 15 / t: 18)
High
(c: 22 / t: 16)
High
(c: 21 / t: 32)
High
(c: 10 / t: 14)
High
(c: 50 / t: 55)
Very poor
(c: 20 / t: 25)
Very poor
(c: 11 / t: 36)
Very poor
(c: 18 / t: 37)
Very poor
(c: 30 / t: 34)
Very poor
(c: 59 / t: 61)
Very poor
(c: 50 / t: 55)
-
-
-
-
-
90
These questions pursue an understanding the needs and the perceived
behavior of drivers during nighttime driving. The results presented on Table 4.1
highlights the comparison between the survey results carried out with car (c)
and truck (t) drivers in various road conditions. The road conditions in which
there are substantial differences in behavior are: town, motorway and rural.
Each one of these conditions will be explained in detail below. The criteria
which were developed to compare the driver’s behavior under these
circumstances are those described on the field categories on Table 4.1.
The overall lighting perception during nighttime driving differs from truck
to car drivers mainly due to the fact that more than 70% of the truck drivers
recommend a change in the current scenario due to the current system
performance to improve its visibility.
On the turning aspect (crossroads, question 5, for example), the truck
driver is more concerned than the car driver (86% versus 74% in town scenario)
due to the size of his vehicle and time of response. On the other hand,
regarding the road visibility ahead the car driver understands that it is more
critical for him due to the limited field of view when compared to that of the truck
driver mainly due to the size of the windshield and the driver´s height when
seated.
The usage of the low beam is similar for both car and truck driver on all
different scenarios. However, when discussing the high beam usage the truck
driver is more concerned if any event in front of the driver happens due to its
longer time of response and also uses it more frequently than the car driver in
all scenarios. In some cases, the driver has both the low and high beams on, so
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that is why under some circumstances the usage surpasses the 100%.
Unfortunately, we can also observe usage below 100%, mainly in town driving,
indicating that under some circumstances no beam is used at all.
Also, the average speed is quite different from the car and truck drivers,
especially on rural and motorways. The average speed is almost 25% less in a
truck than a car and the truck driver seldom surpasses 80 km/h when driving
with cargo.
On motorways, already shown in section 4.2, road conditions are key
operational factors for the truck driver, whose time of response is longer and
therefore he should certainly be able to “see more” and thus be able to avoid
potential accidents. In the town scenario, both car and truck drivers have the
same needs, i.e. city lighting is inadequate and they have to cope with
conditions as presented. Furthermore, the speed is reduced due to the limits
imposed in the city. In the rural scenario, considering that there is almost no
light from the environment, both types of drivers need to rely on their beam
patterns. This is the reason why, in this scenario, the usage of high beam is
greater than in the other scenarios.
On the beam pattern light distribution, for instance, the car driver has the
technology and design to help his perception. Due to the vehicle’s design, the
headlamp shape on cars allows the light distribution to have a greater aperture
making the road visibility under different scenarios better than that of the truck
driver due to the limitation on the design and shape of the truck’s headlamps.
Glare also impacts both types of drivers severely by creating huge
discomfort. It is worse for the truck driver due to the time of response and the
size of the truck involved.
92
Under different weather conditions, such as rain and fog, the car driver
has an edge due to the fact that not only do cars have more fog lamps available
as standard items or aftermarket accessories, but also because the driver’s
visibility is better due to front and rear wipers. From the survey results, more
than 84% of truck and 83% of car drivers consider that road conditions are very
poor in fog when driving on motorways, which is the worst scenario for the
driver due to the lack of road visibility ahead and natural light. In addition to this,
86% of the truck drivers have concerns in rainy conditions, especially when
driving on motorways.
More than 75% of the interviewed drivers in the survey complained about
the lack of foreground and near side illumination in front of the vehicle. This
foreground (20 meters in front of the vehicle) allows the driver to understand
and foresee the road conditions and environment. The near side lighting on
both sides is a key factor in turning, parking and on crossroads. It helps the
driver to see more, aiding these maneuvers.
When there are pedestrians, cyclists or animals on the road, the truck
driver has more difficulty in handling them due to his reduced front field of vision
linked with his longer time of response. In rural areas, the number of animals is
huge and can potentially cause accidents due to the fact that they can appear
from the side of the roads and highways. Pedestrians and cyclists in the city
and on the highways may also create awkward situations which could lead to
accidents. More than 77% of the truck drivers consider cyclists and motorcycle
drivers to be contributors to accidents, especially at nighttime in towns when
their field of vision and visibility are reduced. It must be highlighted that São
Paulo has a huge fleet, more than 750,000 motorcycles, and the largest in
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Brazil. This could also generate noise factors or additional distractions to the
drivers (Fenabrave, 2008).
The truck segment of the market currently lacks the attention of lighting
specialists although there is huge potential for improvement and the fleet size is
growing. Nowadays, fleet owners are looking for improvements to their vehicles
to secure the safe transportation of road freight. The critical analysis of the data
collected from this survey has enabled the development of a beam pattern (light
distribution) for both low and high beam which meets the needs identified by the
truck driver. Moreover, an understanding of both car and truck driver needs and
behavior during day and night time driving in varying road and environment
conditions enables the specialist to incorporate this knowledge into the
definition of the entire lighting system. These aspects may assist the optical
engineer in beam definition to address the needs, beam pattern, LED usage
and AFS system parameters. The impact of glare from oncoming traffic as well
as the average speed, low and high beam usage linked with the noise factors
such as rain and fog are very helpful to describe the needs for each specific
driver. The usage in different scenarios is absolutely critical to define the
characteristics of each driver when exposed to different scenarios. Furthermore,
the analysis of the road conditions and environment helps the specialist to
generate a model with the data from this survey.
4.4
Test track measurements
The final source of information included measurements carried out on the
test track shown in Figure 4.8 (São Paulo, Brazil). This test track represents
standard road conditions and driver behavior. Data acquisition was possible due
94
to the VBOX III device, which features a powerful new Global Positioning
System (GPS) engine capable of providing 100 Hz update rate of all GPS
parameters including speed, direction and position. Velocity and heading data
are calculated from Doppler shift in the GPS carrier signal to provide high
accuracy. It allows the acquisition of fifty measurements per second, with each
measurement comprised of both speed and position (longitude and latitude).
This provides precise information about the driver’s behavior before, during and
after the curve entrance. In addition, during the data acquisition four laps were
done with a car and a truck in order to compare different speeds and scenarios
to gather different responses in an attempt to understand the driver’s behavior
when approaching and leaving different curves.
Figure 8: Test track.
4.5
Modeling and simulation results
After the acquisition of the test track data together with the road scenario
and the survey data, the specialist had sufficient information to generate the
95
model and simulate it with trucks and cars to compare the two kinds of vehicles.
Thus, the development of the model used to control the LED AFS was based on
the following aspects: the road scenario and environment, the survey with
drivers and the data gathered at the test track. The data was then used in the
parameter estimation procedure with a fuzzy model structure.
Fuzzy logic is a superset of conventional (Boolean) logic that has been
extended to handle the concept of partial truth (values between "completely
true" and "completely false"), particularly important when a multi-parameter
decision must be taken (Bellman and Zadeh, 1970). The most important feature
of fuzzy control is that its internal mathematical model is built-up directly through
expertise from human reasoning which can be expressed by a set of heuristic
rules which are quantified according to fuzzy set theory (Mendel, 2001). The
traditional fuzzy inference system (type-1) comprises the use of type-1 fuzzy
sets that is a generalization of a crisp set or zero-one membership function
(Mendel, 2001). A crisp set A, denoted by µA(x), is such that:
1 if x ∈ A
A = µ A (x ) = 
0 if x ∉ A
(1)
A type-1 fuzzy set F is defined on a universe of discourse X (the range of
possible values for the variable x) and its membership function, denoted by
µF(x), takes on values in the interval [0, 1]. A membership function provides a
measure of degree of similarity of an element in X to the fuzzy set F. The rules
of a linguistic fuzzy model have the general form:
If x is A then y is B
(2)
The proposition “x is A” is the antecedent of the rule, and the proposition
“y is B” is the consequent. Variables x and y are linguistic ones defined as fuzzy
96
sets on domains (universe of discourses) X and Y, respectively. Constants A
and B are linguistic terms usually associated with meanings for the linguistic
variables, such as “low temperature”, “high velocity”, etc. Based on Lukacs et al.
(2009) a type-1 fuzzy approach was adopted to develop a model that describes
the relationship between the inputs vehicle speed (km/h) and curvature radius
(m) and the output (electric current applied to drive the LED array). The model
structure considered in this work was based on the TSK (Takagi-Sugeno-Kang)
approach inference model (Liang et al., 2000; Mendel, 2001; Lukacs et al.,
2009) in such a way that the electric current value in each rule is directly related
to the input values through a linear parametric model. In order to showcase this
functionality, the strategy adopted was to measure two signals, the road
curvature and the vehicle speed measured directly from the vehicle,
determining the lighting level of each LED. Figure 4.9 describes the hardware
adopted. The total amount of luminous flux is linearly correlated with the total
current established by the fuzzy model. Each increment of 350 mA on the
current lights totally one LED and a sequential pattern from one to five LEDs is
followed comprising the LED array proposed. There are several methods to
investigate the luminance and measurements on the road. For example, the
quantity and the quality of road lighting can be analyzed and controlled with
road surface luminance measurements. As a reference, the European standard
for road lighting calculations EN 13201-3 (EN, 2003) describes methods for
luminance calculations and measurements (Ekriasa et al., 2008). Light
distribution is represented in terms of luminous intensity and illuminance
patterns. In accordance with the legal requirements, the values for illuminance
are measured at a distance of 25 m (with the exception of Japan and the United
97
States). The illuminance distribution is assessed on a wall at a distance of 10
meters in front of the headlamp as an additional aid to the discussion and
interpretation of the characteristics of light functions (Wördenweber et al.,
2007).
The evaluation of the fuzzy inference in the AFS truck control system
was carried out through the comparison between model prediction and the
specialist response, using the data obtained from test track measurements
(Figure
4.8).
The
general
procedure
comprises
a
comprehensible
representation of the system structure to investigate the model performance
and each input variable (linguistic variable) is featured by three fuzzy sets that
represent three meanings or linguistic terms: small/low, medium and large/high.
As expected, the fuzzy models identified and adjusted for cars (Lukacs et al.,
2009) based on membership functions and specialist rules proposed for this
case did not present satisfactory results for trucks due to the inherent different
speeds and driving conditions. The predominant flat surface presented in Figure
4.10 shows that the car developed model do not reach the full output current
and when applied to trucks does not provide suitable luminosity, according to
the current LED values. There is almost no correlation with responses due to
small-radius curves and reduced speed variants.
98
Le
d
5
400
Le
d
4
320
Le
d
3
240
Le
d
2
160
Fuzzy model
Le
Total
electrical
current
d
1
80
Speed
350
700
Radius of
the curve
1050
1400
1750
Total
electrical
current (mA)
Figure 4.9: System block diagram.
Figure 4.10: Velocity/radius/LED-current surface generated for trucks using the
fuzzy logic car model proposed by Lukacs et al. (2009).
In order to tailor the model to incorporate the needs of the truck driver,
Figure 4.11 presents the membership functions specifically proposed for trucks.
Moreover, the specialist rules based on actual knowledge had to be revised and
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are presented in Table 4.2. Comparing them with the rules used for a car (Table
4.3) by Lukacs et al. (2009), Table 4.2 shows adjustments for the speed and
radius, which are mainly based on truck driver needs. It reflects the survey data
which clearly shows that the average speed in all three conditions, rural, city
and motorway is reduced.
(a)
(b)
Figure 4.11: Membership functions related to the radius (a) and to the speed
(b).
Table 4.2: Antecedents combination and desired output (set by human
specialist) – revised for truck driver needs
#
Rules for the antecedents
Current to LEDs (mA)
(set by human specialist)
1
If speed < 45 km/h and radius < 40 m
1,750
2
If speed < 45 km/h and 40 m < radius < 250 m
1,400
3
If speed < 45 km/h and radius > 250 m
1,050
4
If 45 km/h < speed < 65 km/h and radius < 40 m
1,350
5
If 45 km/h < speed < 65 km/h and 40 m < radius < 250 m
1,000
6
If 45 km/h < speed < 65 km/h and radius > 250 m
700
7
If speed > 65 km/h and radius > 250 m
350
8
If speed > 65 km/h and 40 m < radius < 250 m
0
100
Table 4.3: Antecedent combination and desired output (set by human specialist)
– based on car driver needs
#
Rules for the antecedents
Current to LEDs (mA)
(set by human specialist)
1
If speed < 40 km/h and radius < 50 m
1,750
2
If speed < 40 km/h and 50 m < radius < 280 m
1,400
3
If speed < 40 km/h and radius > 280 m
1,050
4
If 40 km/h < speed < 90 km/h and radius < 50 m
1,350
5
If 40 km/h < speed < 90 km/h and 50 m < radius < 280 m
1,000
6
If 40 km/h < speed < 90 km/h and radius > 280 m
700
7
If speed > 90 km/h and radius > 280 m
350
8
If speed > 90 km/h and 50 m < radius < 280 m
0
The best fuzzy model structure obtained for a car was selected to be
fitted for truck AFS control model. This structure (TSK model) is presented in
Table 4.4. The rules were directly proposed from the knowledge presented in
Table 4.2 joining the membership functions of Figure 4.11 also defined for
trucks according to the specialist. The modeling of this problem using a fuzzy
structure enables the obtainment of a model that provides continuous values for
the electrical current and is also capable to consider the uncertainty associated
to the definition of the membership grade of radius and speed values with
relation to each range (set) defined by the specialist.
The preliminary study carried out by Lukacs et al. (2009) identified that
speed was the main contributor in the output model. The analysis confirmed
that the radius had an insignificant influence on the output. The results captured
with the radius as the main contribution on the output model presented the
101
worst results and had one of the highest quadratic errors. In addition, this
inference supported the fact that the speed was not only more sensitive to the
driver approaching the curves but it was also confirmed by the data collected
from the test track. In order to adjust the fuzzy model performance, the
parameters presented in the rule consequents were estimated through the
solution of an optimization problem that minimizes the deviations between the
model predictions and the desired current value defined by the specialist. This
parameter estimation procedure keeps the intrinsic features of the fuzzy model,
capable of providing continuous values for the electrical current, and enables
the model to be adherent or close to the specialist knowledge. It is important to
emphasize that that model was constrained in order to provide an output at
greatest 10% lower than the specialist guess, which provides a more
comfortable light distribution for the driver. The 10% restriction produced better
results because it presented reduced fluctuations (small variance) and ensured
the best outcome with a smaller quadratic error.
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Table 4.4: The fuzzy model – truck AFS control (parameters a0 j and a1 j ,
j = 1,… ,8 )
Rule base
1. If speed is low and Radius is large then y = a01 + a11 ⋅ V
2. If speed is low and Radius is medium then y = a02 + a12 ⋅V
3. If speed is low and Radius is small then y = a03 + a13 ⋅ V
4. If speed is medium and Radius is large then y = a04 + a14 ⋅ V
5. If speed is medium and Radius is medium then y = a05 + a15 ⋅ V
6. If speed is medium and Radius is small then y = a06 + a16 ⋅V
7. If speed is high and Radius is large then y = a07 + a17 ⋅V
8. If speed is high and Radius is medium then y = a08 + a18 ⋅ V
The mean quadratic error (between model predictions and specialist
response) obtained for the model 1 adjusted was 10,935.96. Although this value
is higher than the model created for the car (model 2), the surface that was
generated to the truck driver is smoother when compared to the one generated
with the car model adopting the truck needs. Table 4.5 shows the best solution
for the car and truck driver adopting type-1 fuzzy.
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Table 4.5: Best output models adopted for car and truck driver
Model
Fuzzy
Output model
Type
Quadratic
Std
Error
Deviation
1
1
y = a0 + a1 ⋅ V
10,935.96
474.63
2
1
y = a0 + a1 ⋅ V
5,061.11
322.33
Figure 4.12a shows the comparison between the results predicted by the
model and the specialist suggestion. Figure 4.12b shows the surfaces
generated by the model highlighting that the revisions in the rules solved the
discrepancies observed when the car model was applied to trucks (Figure 4.10).
The revised truck model with fuzzy structure describes a smoother surface,
highlighting the circumstances in which the LEDs will operate and showing a
good correlation between the radius and speed under small curves and radius.
Confirming the coherence of the model, Figure 4.12b presents a ramp showing
that current increases when radius and speed decrease and that there is a
decrease in current when speed and radius increase. These circumstances are
critical for the truck driver due to the lack of illumination on the roads and in the
environments described earlier. The existence of a non-uniform surface with
some abrupt drops in the current is expected and is associated to the
nonlinearity of the fuzzy model and its intrinsic ability to interpolate different
operational ranges. As stated earlier, a 10% restriction was suggested by the
specialist to ensure that the model will not show a discrepancy within 10% of its
best knowledge keeping the required amount of light on the road. The
comparison of the surfaces allowed the specialist to confirm that this restriction
is necessary to guarantee the correct amount of light for the driver, since the
driver will have a safer night time whenever there is sufficient illumination on the
104
road ahead. Due to Brazilian road and environment conditions, this restriction is
mandatory in order to guarantee the additional amount of light generated by the
LED AFS system.
(a)
(b)
Figure 4.12: (a) Comparison between the LED array current set by the specialist
and the same parameter determined by model 1. (b) Velocity/radius/LEDcurrent surface generated by the revised truck model 1.
The surface of Figure 4.12b comprises a more continuous behavior than
the surface on Figure 4.13 (specialist surface). Furthermore, the surface of
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Figure 4.12b presents a homogenous feature that also contributes to reduce the
risk of possible distractions (or mistakes) of the truck driver during the nighttime
driving. The comparison between Figures 4.12b and 4.13 is enough to justify
the adoption of a modeling procedure, suitable for the reality described, instead
of the specialist rules itself.
Figure 4.13: Surface generated by the Table 2 (specialist rules for the truck
driver).
It is important to highlight that the surface in figure 4.13 is flat when the
speed is greater than 60 km/h reassuring that the surface in figure 4.12b gives a
better light output to the driver and generating a better nighttime vision, without
abrupt ruptures in the surface.
4.6
Conclusions
This paper describes the needs and behaviors of both truck and car
drivers concerning road illumination in São Paulo, Brazil. The study has
generated considerable data which provides information that can enhance the
development of a tailored LED AFS system taking into account not only the
106
survey data but also the road conditions and the results from the simulations
analyzed. The input given to the specialist helped define the intensity and
distribution of light required for a driver when an AFS system is available. It is of
utmost importance to highlight that a specific a fuzzy model is needed to
address the needs of truck drivers. The proposed model confirms that the car
model cannot be used to address truck driver needs. This study showed that
the truck driver needs are different from those of the car driver, not only
because of the field of vision and time of response, but also because the truck
driver’s expectations of illumination are different, as highlighted in section 4.3.
So far, there have been no studies in this field and this paper acknowledges
that there are many aspects yet to be investigated. Furthermore, the data
collected in this study enables the development of a tailored light distribution for
low and high beams for both car and truck drivers in the Brazilian market.
The proposed truck AFS LED system (five LED array with optics,
electronic power driving system, onboard micro-controller and fuzzy logic builtin software controller) to enhance night time driving has been developed
embracing a modeling framework which comprises a combination of expert
knowledge and a parameter estimation procedure that enables the adjustment
of the output models of each rule. More specifically, specialist engineering
judgment can be incorporated into the fuzzy rule base according to knowledge
and experience of night time and environmental driving conditions.
Finally, there should be additional research in this field in particular to
develop this AFS LED system to be incorporated into the truck segment of the
market, either as an accessory or as an optional feature from the OEM. A
patent for this proposed system is being requested due to its innovative
107
approach in the Brazilian market. It would be an Industry First and would raise
the technology bar to the next level in the truck segment.
Nomenclature
aij
model parameter
y
current to LEDs (mA)
V
velocity of the vehicle (km/h)
OEM
Original Equipment Manufacturer
References
Bellman, R. E., Zadeh, L. A., 1970. Decision-making in a fuzzy environment,
Management Science 17, 141–164.
Burg, A., 1967. Light sensitivity as related to age and sex. Perceptual and Motor
Skills, 24 (3), 1279-1288.
Egberink, H. O., Lourens, P. F., van der Molen, H. H., 1986. Driving strategies
among younger and older drivers when encountering children. Accident
Analysis and Prevention, 18 (4), 315-324.
Ekriasa, A., Eloholma, M., Halonen, L., Song, X., Zhang, X., Wen, Y., 2008.
Road lighting and headlights: Luminance measurements and automobile
lighting simulations, Building and Environment 43, 530–536.
EN, 2003. Road lighting—Part 3: Calculation of performance, European
standard EN 13201-3, Publication 270-2003, Ref. No. EN 13201-3: 2003 E.
Fenabrave, 2008. Anuário da Distribuição de Veículos Automotores do Brasil
2008, Fenabrave – Federação Nacional da Distribuição de Veículos
Automotores. website: www.denatran.gov.br.
108
Gish, K. V., 2001. Driver behavior and performance using an infrared night
vision enhancement system. Technical Report, DTHN22-95-D-07019.
National Highway Traffic Safety Administration.
Intervias, 2000. Research Regarding the Accidents of the Highways of the State
of São Paulo (http://www.intervias.com.br).
Karnik, N. N., Mendel, J. M., 1998. Introduction to Type-2 Fuzzy Logic Systems,
Liang, Q., Karnik, N. N., Mendel, J. M., 2000. Connection Admission Control in
ATM Networks Using Survey-based Type-2 Fuzzy Logic Systems, IEEE
Transactions on Systems, Man and Cybernetics, Part C 30, 329-339.
Liu, A., Veltri, L., Pentland, A. P., 1998. Modelling changes in eye fixation
patterns while driving. Vision in Vehicles, 6, 13-20. Elsevier, Amsterdam.
Lukacs, L., Fontes, C., Embiruçu, M., Pepe, I., 2009. Implications and outcomes
of controlling a LED AFS (Adaptive Front-Lighting System) using fuzzy logic
for the South American markets, SAE Technical Paper Series, No. 2009-360027.
MapLink, web program based with worldwide maps, 2008. Website:
http://maplink.uol.com.br. Copyright 2008.
MATLAB software, version 7, fuzzy application toolbox, 2005. The Mathworks,
Inc, Natick, Mass.
Mendel, J. M., 2001. Uncertain Rule-based Fuzzy Logic Systems: Introduction
and New Directions, Prentice-Hall P. T. R., London, UK.
Neumann, R., 2004. AFS halogen headlamp system: Experimental study and
first field results, SAE Technical Paper Series, No. 2004-01-0439.
109
Okombi-Diba, B. R., Okuwa, M., Uchiyama, Y., Kozato, A., Hongo, T., 2007.
Evaluation of a night driver support system: Driver eye movement behavior.
Journal of Computing in Civil Engineering 21(3), 200-210.
Wördenweber, B., Wallaschek, J., Boyce, P., Donald D. Hoffman, D., 2007.
Automotive Lighting and Human Vision, Springer Verlag, USA.
110
Capítulo 5
Desenvolvimento do protótipo do AFS baseado em
LEDs
Apresentação
Este capítulo apresenta o desenvolvimento do protótipo pelo viés
experimental. É uma continuação dos capítulos anteriores, onde as definições
de qual LED deveria ser adotado e de qual lógica fuzzy deveria der adotada,
em função das condições das estradas, do meio ambiente e dos resultados da
pesquisa, ajudaram o especialista a desenvolver o modelo. Todos estes
aspectos tratados anteriormente permitiram o desenvolvimento do protótipo
físico (teste de bancada).
Resumo
Este capítulo discute o processo de prototipagem experimental, iniciando
com o projeto do AFS com LED, incluindo o desenvolvimento do componente
até a definição do produto e de sua funcionalidade. Depois disso, o foco é no
impacto e no beneficio para o consumidor final, já que o objetivo principal deste
estudo é criar uma condição de dirigibilidade noturna mais segura. Três
aspectos chaves serão analisados e levados em consideração: o produto, a
integração do sistema e as implicações futuras.
Palavras-chave: AFS, LED, iluminação, lógica fuzzy, controle
111
Conclusão
Este capítulo apresenta o desenvolvimento bem sucedido do protótipo,
controlado pelo modelo proposto, que utiliza o conhecimento do especialista,
que foi construído e desenvolvido em etapas anteriores. O protótipo é
consistente com o conceito desenvolvido do sensor angular da direção e da
velocidade do veículo. Tanto o LED como a ótica da lente foram escolhidos
para serem incluídos no protótipo veicular. Além disso, a posição e o ângulo da
posição dos LEDs também foram definidos. O software para controlar este
sistema, adotando Visual Basic, foi desenvolvido e funcionou perfeitamente
com o protótipo de bancada e com o protótipo veicular.
No campo da eletrônica, destacam-se algumas realizações, tais como: o
conceito inicial da eletrônica de potência, o micro controlador e a unidade de
controle. Este desenvolvimento foi muito importante para poder integrar os
sistemas elétrico e mecânico. Este integração mostra o desenvolvimento
mecatrônico alcançado.
Portanto, os resultados e o desempenho do protótipo comprovam uma
contribuição tecnológica expressiva neste estudo, mostrando a viabilidade de
um sistema AFS com LEDs e desafiando a equipe de trabalho a perseguir
desenvolvimentos futuros a fim de desenvolver a aplicação veicular. Além
disso, existem estudos em desenvolvimento com duas companhias, uma nos
Estados Unidos e outra no Brasil. O apêndice C exibe uma carta da “De
Ameretek Corporation”, localizada em Chicago, e também o esquema inicial
para uma primeira proposta do produto. No Brasil, discussões técnicas com a
“Rontan do Brasil” também já se iniciaram. Este é um feito importante, devido
112
ao fato de que existem companhias interessadas em desenvolver este produto
para a indústria automotiva aqui no Brasil e também nos Estados Unidos.
113
Chapter 5
Developing the LED based AFS prototype
Opening statement
This chapter presents the prototype development process from the
experimental point of view. It is a continuation from the previous chapters where
the definitions regarding which LEDs should be chosen, which fuzzy logic
should be adopted and which road conditions, environment and the survey
results helped the specialist develop the model. The prior treatment of these
aspects allowed the development of the physical prototype (workbench test).
Abstract
This chapter discusses the experimental prototyping process, starting
with the LED AFS design, including the device development until the definition
of the product and its functionality. The focus which will be adopted will be on
the impact and benefits for the customer, since the main goal of the proposal is
to make night time driving safer by adding light. Three key aspects will be
analyzed and taken into account: product, system integration and further
applications.
Keywords: AFS, LED, lighting, fuzzy logic, control
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5.1 Introduction
There are three main aspects in this work: product, system integration &
application and the customer acceptance criteria. The Brazilian automotive
market is still very much an “emerging” market, with regards to widespread
“new” technology uses, given its initial cost. Therefore, there will be a detailed
explanation on customer needs and how this directly impacts on the system’s
choice and definition, as well as a discussion on the suitability of commercial
applications suited for AFS.
There have been a series of publications such as technical papers that
highlight the advantages of incorporating an AFS system to a passenger car
(Hogrefe, 2000; Ishiguro et al., 2004; Neumann 2004). However, up until now,
the heavy trucks market has not been considered a candidate for its use.
AFS is designed to incorporate two independent light sources: a highoutput halogen projector for the main beam and a secondary row of light
emitting
diodes
(LEDs)
that
illuminates
almost
instantaneously.
This
combination distributes the light beam evenly and consumes less power than
conventional lights. The technical challenge comprises the effective control of
its use.
5.2 Looking for a product
As mentioned earlier, the first major aspect is to properly define the
product and its components. As an initial approach (as if from a blank sheet of
paper), the first on-vehicle AFS prototype developed for this research could be
divided into the following components: light source, optics, heat sink and
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connectors. The light source that was chosen for this project is a diode
commonly known as light emitting diode (LED).
5.2.1 LEDs
A LED is a semiconductor device that emits visible light when an electric
current passes through it. From the early seventies up to mid nineties, the LED
light output was not particularly bright, and most LEDs were monochromatic
light sources, with light emission occurring around a single wavelength. The
output light from those diodes goes from red (approximately 700 nanometers) to
blue-violet
(around
400
nanometers).
Some
LEDs,
usually
used for
communication applications, can emit at infrared (IR) mode, typically 830 nm
and 1200 nm.
A LED consists of two elements of processed material called “P-type”
and “N-type semiconductors”. These two elements or materials are grown in
direct contact forming a region called the “P-N junction”, where a depletion zone
(deprived of a free electric charge) is formed. In this respect, the LED
resembles other diode types, but there are some important differences. The
junction is formed mostly on the middle of the thin N and P layers and the whole
LED has a transparent package. The thin material and the special package
allow for visible or IR photons which are generated on the vicinity of the
depletion zone around the junction and pass through it, coming out from the
device. Also, the LED has a large P-N junction area whose shape is tailored to
improve its light source quality.
During almost three decades, LEDs were related only as a marker or
reference light on equipment panels and displays. In 1993, Nichia Chemical
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from Japan started the production of blue LEDs (Nakamura, 1996). When blue
diode was combined with red and green LEDs, the outcome was white (RGB,
Red Green Blue) light. This was a breakthrough in technology and has opened
a series of debates as well as new developments. The principle of RGB white
composition is sketched in Figures 5.1 and 5.2.
Figure 5.1: Composition of white light through RGB.
Blue
Peak
Red
Peak
Green
Peak
470 525 590 630
(nm)
Figure 5.2: Example of light spectrum for white light source composed by RGB
technique.
On the other hand, companies like OSRAM (Germany) and Lumileds (a
division of Philips) dedicated resources to LED development and adopted other
technologies to obtain white light. The following concept was used: the addition
117
of a phosphorophore composite, capable of emitting from green to red, placed
on the top of the blue LED, used as driver, generating white light. In Figures 5.3
and 5.4 the light composition and spectrum for this kind of white LED is shown.
Phosphorophore
composite layer
Blue LED chip &
Phosphorescence Light
drive
White Light output
Figure 5.3: Composition of white light through blue led adding a
phosphorophore composite.
Combined spectrum
Blue-white
Yellow phosphor
InGaN
Blue LED
470 nm
Blue
525 nm 590 nm 630 nm
Amber Red
Green
Figure 5.4: White phosphorophore LED spectrum.
Some benefits of LEDs, when compared with incandescent and
fluorescent illuminating devices, include:
•
Low power requirement (less then 5 W per LED);
118
•
High efficiency;
•
Long life;
•
Instant response.
In order to choose which LED was the most appropriate to adopt, a
series of laboratory experiments have been performed. There were three
samples to be evaluated. Two samples from Lumileds and one from OSRAM.
From OSRAM, the team evaluated the Golden Dragon from 50 to 100 lumens,
and the Lumileds LUXEON I and K2 varying in the same range as well. The
main difference is that the electric current to power up Lumileds is 1000 mA and
the OSRAM´s need 350 mA instead. As an example, Figure 5.5 describes the
construction of a LUXEON®K2 Power LED with the details and its components.
Figure 5.5: Construction of a LUXEON®K2 Power LED (Thermal Heatsink
extracts heat from the LED chip).
As a result of the AFS developing process, two bench prototypes were
created to evaluate the samples. The block diagram for one LED channel, used
on composing the AFS system, is presented in Figure 5.6.
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Microcontroller
LED
Optics
Heat Sink
Power
electronics
AFS controlling
software
Sensor system
Figure 5.6: Block diagram for one LED channel.
5.2.2 Optics
In order to define the optics, three different lenses with different aperture
angles were used. The samples (KEPL197XX Series) were provided by the
Italian company Khatod (Khatod, 2009), its datasheet can be found on the
Khatod's website. The samples that are shown in Figure 5.7 had 10, 25 and 40
degrees of aperture, respectively. The best solution for the proposed AFS
application was the 10-degree aperture due to the sharp light cone edges.
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Figure 5.7: Lens with holder with 10, 25 and 40 degrees of aperture (left to
right).
5.2.3 The selected LED
On chapter 2, the comparison among high power LEDs showed the best
results towards the OSRAM Golden Dragon for this application. However,
silicone is a soft material and prone to attracting dust. These properties make
proper handling imperative to avoid damage to the emitters. In Figures 5.8 and
5.9 there are examples on the K2 LED (Philips, 2009) associated to the Khatod
lens. Lifetime for solid state lighting devices (LEDs) is typically defined in terms
of lumen maintenance: the percentage of initial light output remaining after a
specified period of time. Philips Lumileds projects that white LUXEON K2
products will deliver, on average, 70% lumen maintenance at 50,000 hours of
operation at a forward current of 1000 mA. This projection is based on constant
current operation with junction temperature maintained at or below 120 °C.
Figure 5.8: LUXEON®K2 Power LED from Lumileds.
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Figure 5.9: LUXEON®K2 Power LED with Khatod holder and lens.
5.2.4 Heat sinking
As already stated, lumen maintenance is directly associated to the
internal blue LED wafer (chip) operation temperature. When on rush operation
or polarized by its quiescent current, it is imperative to have a way to evacuate
calories from the LED. The heat sinking system adopted for the first bench
prototype was chosen from standard catalog of HS Dissipadores (HS, 2009), a
Brazilian company. Figure 5.10 shows the “HS3542” profile chosen and used on
the prototype. The datasheet with all the technical data is available on the HS's
website.
Figure 5.10: HS3542 profile dimensions in mm.
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5.2.5 The power electronics
The power electronic block in Figure 5.11 is based on the IRFZ46N, a
HEXFET® power MOSFETs produced by International Rectifier.
Figure 5.11: Basic HEXFET structure.
The option on power up the AFS LED array using an FET transistor
resides on the fact that the power management efficiency of those devices is
much better than the bi-polar transistors. Figure 5.12 shows the basic electronic
circuit for one LED AFS channel.
123
LED ARRAY
Figure 5.12: Basic one channel LED AFS power circuit.
International
Rectifier
has
provided
rugged
Power
MOSFET
semiconductor devices for almost 20 years. To better understand and utilize IR
HEXFET Power MOSFETs, it is important to explore the rugged MOSFETs
design. All semiconductor devices are rated for a certain max reverse voltage
(BVDSS for Power MOSFETs). Operation above this threshold will cause high
electric fields in reversed biased P-N junctions. Due to impact ionization, the
high electric fields create electron-hole pairs that undergo a multiplication effect
leading to increased current. The reverse current flow through the device
causes high power dissipation, associated temperature rise and potential device
destruction.
Despite the fact that MOSFETS are more efficient on energy
management, all semiconductor devices contain parasitic components intrinsic
to its physical structure. In Power MOSFETs, these components include
capacitors due to displaced charge in the junction between P and N regions,
resistors associated with material resistivity (for example RB), a body diode
formed where the P+ body diffusion is made into the N- epi-layer, and a virtual
124
NPN parasite bi-polar junction transistor (BJT) formed where the N+ source
contact is diffused.
First introduced in the mid-1980’s, Avalanche Rugged MOSFETs and
HEXFET are designed to avoid turning on the parasitic BJT until very high
temperatures and/or if a very high avalanche current occurs. This is achieved
by:
•
Reducing the P+ region resistance with higher doping diffusion;
•
Optimizing cell/line layout to minimize the “length” of RB.
The net effect is a reduction of RB, and thus the voltage drop necessary
to forward bias the parasitic BJT will only occur at higher current and
temperature.
Avalanche Rugged MOSFETs are designed to contain no single
consistently weak spot, so avalanche occurs uniformly across the device
surface until failure occurs randomly in the active area. Utilizing the parallel
design of cells, avalanche current is shared among many cells and failure will
occur at a higher current than for designs with a single weak spot. Figure 5.12
shows the basic device structure.
A Power MOSFET which is well designed for ruggedness will only fail
when the temperature substantially exceeds rated TJMAX.
The
voltage
and
current
regulation
is
done
by
the
National
Semiconductors LM317, a 3–terminal floating regulator. In operation, the LM317
develops and maintains a nominal 1.25 V reference voltage (Vref) between its
output and adjustment terminals. This reference voltage passing through a
125
resistor (R1) is converted into a programming current (IPROG) (see Figure 5.13),
and this constant current flows through R2 to the ground. The regulated output
voltage is given by:
(1)
Figure 5.13: LM317 semiconductor application.
Since the LM317 is a floating regulator, it is only the differential voltage
across the circuit which is important to performance, and operation at high
voltages with respect to ground is possible.
5.2.5.1 Connectors
The connector which was adopted for both the vehicle and bench
prototypes was from Yazaki and is an industry standard sealed connector
(Yazaki, 2009). It is already used within the OEMs. This connector contains 8
outputs which are used to control the LEDs independently.
126
Sealed connectors have evolved from decades of intensive design
refinement. Their main characteristic is their protection from both moisture and
other contaminants. Terminal blade sizes from Yazaki samples are in
accordance with USCAR and ISO standards. In Figure 5.14 there is an example
from a sealed connector.
Figure 5.14: Example of a sealed connector.
Moreover, the Terminal Position Assurance Device is available frontloaded and preset to the connector housing. The connector cannot be mated if
preset terminal spacer is not in the full-lock position. The Connector Position
Assurance Device is available preset to the female terminal connector housing.
Yazaki series is compatible with a wide range of wire sizes and current gauge
requirements. In Figure 5.15 there is an exploded view of a sealed connector
(Yazaki, 2009).
127
Figure 5.15: Exploded view of a sealed connector.
With the increasing demands of performance and system functionality,
new header connectors are designed to be a robust automotive interconnection
solution. In Figure 5.16 there is a connector drawing that was adopted for this
study, which is an industry standard.
Figure 5.16: Sealed connector side view.
In Figure 5.17 the header is shown with the technical details from the
supplier.
128
(a)
(b)
Figure 5.17: (a) Sealed connector technical datasheet from header; (b) Detail X.
5.2.6 The microcontroller
A Microcontroller (µC) is a small digital computer on a single integrated
circuit. It is usually based on two architectures: von Neumann or Harvard. The
von Neumann architecture is a design model for a stored-program computing
single system that uses a processing unit and a single separate storage
structure (memory) to hold both instructions and data. Despite the fact that the
original plan was to use a microcontroller device to drive the proposed AFS
system, in this stage a PC will be used to simulate the microcontroller on the
129
bench prototype development. As a future development, a microcontroller will
be defined for the vehicle level study.
5.2.7 The controlling software
As the primary goal of this research is the embedded software (AFS
controlling routines) and hardware (AFS microcontroller and power circuitry) in
vehicles, the development used a PC and a high level code writing
programming language.
The programming template adopted to implement the controlling
software was Visual Basic (VB). Visual Basic is a Microsoft environment
allowing object-oriented programming capable to produce a graphical user
interface. On that human-machine interface (HMI) the user can interact on
choosing and modifying pre-selected sections and parameters, redefining the
way routines and subroutines will run.
Since Visual Basic is easy to learn and fast to write code with, it was
used during prototyping and test. Codes will later be written in a lower level
language, more difficult and hermetic, but more efficient. Visual Basic is also
widely used to write working and commercial programming codes. Microsoft
reports that there are at least 3 million developers using Visual Basic nowadays.
Since its launch in 1990, the Visual Basic approach has become the
standard for programming languages. Now there are visual environments for
many programming languages, including C, C++, Pascal, and Java. Visual
Basic is sometimes called a Rapid Application Development (RAD) system
because it enables programmers to quickly build prototype applications.
130
The program was written to incorporate the fuzzy logic rules translated to
the steering angle and speed. The computer code developed at the laboratory
is added in Appendix B.
5.2.8 The sensor system definition
The definition regarding the steering wheel angle sensor will be divided
in two stages. In the first stage, the strategy adopted was the use of a video
game joystick (featuring a steering wheel) to be able to simulate, in a laboratory,
a car- driving situation. The digital environment used to run the LED AFS
controlling routines was a PC, where a high level language program was
developed with Visual Basic software. The interface to the sensor was already
incorporated to the joystick and will be discussed later on section 5.3.
Although there are some sensors already available in the market, there
are some studies underway to define the sensor that will be adopted for the final
product. Some wireless proposals have been done but the implementation is
not yet completed in order to be adopted in a vehicle. In recent years, the
introduction of Light Emitting Diode (LEDs) and Adaptive Front-lighting Systems
(AFS), which directs the light beam according to the driver’s needs have been
studied. These intelligent systems acquire data from vehicle sensors to
determine the direction of the light beam and its distribution. One of the main
barriers on the development of these systems is to create and install sensors
inside the vehicle without interfering with the other piece of equipments. LaPO
team is developing a wireless sensor to be adopted in any vehicle that can
capture the signal and transfer it to controller as one of the system input.
According to Ribeiro et al. (2008), the use of wireless communication
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technology in the sensor’s data transmission on the interior of the vehicle is a
feasible proposal. A steering sensor was developed to be adaptable to any
vehicle without the need of interfering with the car’s project. It is the objective of
LaPO team to add another vehicle security item without interfering on the
productivity or cost of automotive assembly line. In Figure 5.20, there is an
example of a steering wheel angle sensor which is part of the whole vehicle
prototype development.
Battery 12V
Figure 5.20: Sensor schematics for steering wheel.
5.3 Experimental
In order to develop the first bench prototype, an initial assumption was
developed from the sketch shown in Figure 5.21. It demonstrates the LEDs with
optic lens glued to heat sink.
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Figure 5.21: LED with optics glued to heat sink.
Assuming that this design is a first prototype assembled with LED
OSRAM Golden Dragon, in Figure 5.22 one can see those LEDs glued to the
heat sinker.
Figure 5.22: LED OSRAM Golden Dragon with heat sink.
In order to evaluate both LEDs, this experiment was developed
considering the OSRAM Golden Dragon LEDs on both sides of the AFS. The
used TYCO connectors, an industry standard, were mounted in the way to
allows interconnection and/or swapping during the tests.
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5.3.1 Test setup
This is the proposed layout for the first bench prototype test. It is
important to mention that improving and developing optics were not the main
goal at this stage. It was imperative to achieve control of the system’s
performance according to the model that was developed. In Figure 5.23 there is
a scheme of the proposed prototype.
2
1
3
5
4
Figure 5.23: First bench prototype layout: 1) LEDs LHS; 2) LEDs RHS; 3)
Control and power unit; 4) PC; 5) Joystick with wheel and pedals.
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The prototype is divided into 5 components: LEDs left array, LEDs right
array, control and power unit, the personal computer PC, and the joystick
featuring a steering wheel and pedals (acceleration and breaks). After the
theoretical and numerical simulations using MATLAB software (MathWorks,
Inc.), the algorithm to control the prototype has been implemented on a Visual
Basic programming code. This prototype allowed the algorithm to be both tested
and continuously improved. Results will be presented comparing the simulation
with the prototype.
5.4
Appearance
When examining the actual trend within the available technology and
design perspectives for a given item, it is needless to mention that functionality
is not the only important factor on the market today. It is crucial to highlight that
the next generation of adaptive front lighting system (AFS) features sequential
LED lighting in any case, discarding most of the technological development
done on the past as mechanical solutions. For example, for the prototype
developed as a initial study to add the LEDs in a production lamp, the Ford
Ranger was the chosen part (Figure 5.24). The fashion in which the system’s
optics has been designed to function together has not yet been seen or
appreciated by the exterior lighting world. In addition to being a beautifully
designed optical arrangement, the system also helps drivers take corners and
curves more safely and consumes less energy while doing so due to the LED
usage.
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Figure 5.24: Ranger headlamp prototype with AFS LED incorporated.
The system senses when the vehicle is approaching a curve and directs
the row of LEDs to switch on sequentially. As the vehicle turns, the LEDs
illuminate at a rate and intensity determined by the degree and speed of the
turn. Electronic sensors analyze inputs from the steering wheel and the vehicle
speed to determine how and when to illuminate the LEDs. The LEDs
automatically switch off when the road straightens out, but the main beam
continues to illuminate the overall road. The result is more light, accurately
placed, and more importantly, light is being added for more visibility during night
time driving, not taken from one spot on the road and having it moved to
another, as today's cornering systems do.
The concept that was investigated in this study contains five LEDs which
are lit according to a certain logic incorporated on the algorithm.
5.5 System integration and application
In order to validate the development proposed so far, a bench prototype
was mounted using a PC as the onboard intelligent unit and a joystick as the
car steering wheel. All the details will be presented in the following sections.
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5.5.1 Sensor definition
On the bench prototype, the steering sensor is a potentiometer fixed to
the joystick steering wheel; on the other hand the speeding and the breaking
signals are obtained from two proportional displacement sensors coupled to the
joystick pedals. The PC joystick treats the analog signals converting it to digital
format; the peripheral–computer communication is made through an USB port,
using a proprietary protocol.
5.5.2 Control strategy and benefits
The fuzzy models developed in the chapters 3 and 4 were used to
control the lighting intensity of the LEDs according to the vehicle’s speed and
road’s curvature.
As discussed before, fuzzy logic is derived from fuzzy set theory and
copes with the uncertainty of the information. Uncertainties are present in many
human tasks such as decision making where uncertain, vague, ambiguous or
contradictory terms must be evaluated to reach a decision. Fuzzy logic has
been used widely to model systems providing non usual procedures in order to
obtain information related to the interaction between variables or systematic
approaches to support the decision making required in some operational tasks.
The variables considered in the workbench test, using the physical prototype
described, were two inputs (road curvature and vehicle speed) and one output
signal (LEDs been lit) according to the model and fuzzy rules presented in
chapters 3 and 4.
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The final product not only incorporates the state of the art in the
technology in the market but also launch a product that is Hg free. This product
incorporates all the LEDs advantages, such as long life, more efficient than an
incandescent bulb, lower maintenance costs and better resistance to impact
and vibration. Moreover, it is of utmost importance to highlight that there is a
substantial increase in safety. There is an opportunity to reduce accidents
giving the customer more comfort. In Figure 5.25 the “real gain” in lighting when
adopting this system is shown. Here is key to highlight that the system below
uses the step motor to shift the light instead of adding the light. There are
several limitations to the range as well, since this system can only shift the light
15 degrees to the right and 5 degrees to the left, mainly due to the regulatory
aspect since there are several photometric points to be met and the light still
needs to meet those regulatory points.
Figure 5.25: Real gain (blue zone) versus regular beam pattern (green).
5.6 Simulation and experimental comparison
In order to compare the simulation generated from chapters 3 and 4 from
the fuzzy algorithm, the specialist expertise and the bench prototype, here is the
proposed method to analyze the data acquired from the prototype. As
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previously mentioned, the fuzzy algorithm created on chapter 4 was already
tailored for the truck driver in order to enhance the night vision taking into
consideration not only the driver needs according to the survey but also the
road and environmental conditions. For the data collection a test track was used
and the acquisition was done in real time with the VBOX equipment. To allow
the comparison between the simulated and the specialist, a collection of data
points was done every 2 seconds.
At this point a very important consideration must be done. All simulation
using fuzzy logic (see chapter 3 and 4) was done using MATLAB as operational
template, running in a robust and well configured (hardware and operational
system) personal computer. This numerical machine is more powerful, on
numerical calculation terms, then any (affordable) microcontroller, imposing that
the onboard car system will be simpler then the simulation laboratory system
and the results with one system and another will reflect that limitation.
In order to validate the bench prototype model, the proposed comparison
analyzed the 237 data points which where simulated on the bench prototype
reproducing the same speed and curve radius that were acquired at the test
track and the specialist expertise. Figure 5.26 shows the results when
comparing the outcome theoretical (simulated) current that must be supplied to
the LEDs, the actual sent current decided by the onboard system and the
specialist’s direction. The black curve is the simulated data from the MATLAB
calculated algorithm, the blue one is the specialist input and the red one is the
current sent to the LED by the bench prototype. These results will allow not only
understanding the system's functionality but also go ahead for the development
of the vehicle on board prototype, specifically for the heavy truck segment.
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Figure 5.26: Comparison among prototype, simulation and specialist data.
In order to compare the three results a correlation was done among the
three output signals, specialist, prototype and simulated. In order to compare
the three results, the correlation among the three output signals, two by two,
has been investigated. In the first case, presented in Figure 5.27, there is the
comparison between the simulated and the prototype. After that, the
comparison in Figure 5.28 is between the simulated and specialist data, and in
Figure 5.29 the prototype and simulated data is compared. The correlation is
calculated upon the division of one signal versus the other one. Table 5.1
presents the results with the standard deviation from the data collected.
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Table 5.1: Correlation among specialist, prototype and simulation
Simulated vs.
Simulated vs.
Prototype vs.
Prototype
Specialist
Specialist
Average
1.06
1.11
1.04
Std Deviation
0.15
0.19
0.10
It is clear that there is a strong correlation among the data presented.
The result from the simulation and the prototype indicates that there are some
adjustments to be perceived in the algorithm and in the Visual Basic program in
order to improve this correlation.
141
(a)
(b)
Figure 5.27: Behavior of the correlation between simulated and prototype
(a) and the data comparison (b).
142
(a)
(b)
Figure 5.28: Behavior of the correlation between simulated and specialist
(a) and the data comparison (b).
143
(a)
(b)
Figure 5.29: Behavior of the correlation between specialist and prototype
(a) and the data comparison (b).
If the local high frequency fluctuation is disregarded, it is noticeable that
in all three cases (specialist and prototype, specialist and simulation, simulation
and prototype), the general behavior is the same for those curves. The system
shows a systematic behavior when the current sent to the LED is mostly bigger
than the actual simulated value. This seems to indicate that the control system
has a tendency to over react sending more light to the AFS.
It is important to highlight that there are improvements to be done in both
the simulation and the prototype in order to improve this correlation. However,
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this is already a confirmation that the prototype system is prepared to be taken
to the vehicle level test.
5.7 Conclusions
This chapter presents a prototype successfully developed and controlled
according to the model and specialist knowledge developed and raised in
previous steps. The prototype is also consistent with the concept associated to
the steering wheel angle sensor and speed. Both LED and the optics of the lens
were chosen to be included on the vehicular prototype. Moreover, the position
and angle where the LEDs should be positioned was also defined. The software
to control this system, adopting Visual Basic, was developed and successfully
worked on both the workbench and vehicle’s prototype.
Therefore, the results and the prototype performance attest to the
expressive technological contribution of the whole work, showing the feasibility
for the LED AFS system application and encouraging the work team to generate
further studies and developments within this subject to seek the vehicle
application.
Also, it is important to describe that there are adjustment to the algorithm
and also it confirms that the prototype still supplies more light to the driver
however it does reinforce the fact that the next step will be to evaluate this on
the road where the visual perception will determine the system’s functionality
and a jury evaluation should be held to confirm the improvements that can be
obtained with this system.
There are studies under development with two companies, one in the United
States and one in Brazil. On appendix C, there is a letter from “De Ameretek
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Corporation” located in Chicago and also the initial scheme and first product
proposal. In Brazil, technical discussions with “Rontan do Brasil” have already
started. This is a huge accomplishment due to the fact that there are companies
interested in developing this product for the automotive industry here in Brazil
and in the United States as well.
Nomenclature
USCAR
The United States Council for Automotive Research
ISO
International Standard Organization
LaPO
Laboratory of Optical Proprieties
References
Hogrefe, H., 2000. Adaptive front lighting systems for optimum illumination of
curved roads, highway lanes and other driving situations, SAE Technical
Paper Series, No. 2000-01-0431.
HS, 2009. HS Dissipadores, http://www.hsdissipadores.com.br.
Ishiguro, K., Yamada, Y., 2004. Control technology for bending mode AFS, SAE
Technical Paper Series, No. 2004-01-0441.
Khatod, 2009. www.khatod.com.
Lexikon, 2002. History of Computing, Lexikon Services, ISBN 0-944601-78-2.
Nakamura, S., 1996. High-brightness blue/green LEDs and first III-V nitridebased laser diodes, Proc. SPIE Physics and Simulation of Optoelectronic
Devices 2693, 43-56.
Neumann, R., 2004. AFS halogen headlamp system: Experimental study and
first field results, SAE Technical Paper Series, No. 2004-01-0439.
146
Philips, 2009. http://www.philipslumileds.com.
Ribeiro, M., Simoes, L., Lukacs, L., Pepe, I. M., 2008. Adopting wireless
communication sensor to apply with AFS system. V National Congress of
Mechanical Engineering (CONEM), Bahia, Brazil.
Yazaki, Yazaki datasheet, 2009. http://www.yazaki-na.com.
147
Chapter 6
Conclusions and suggestions for future studies
Opening statement
This chapter presents the final comments on the LED AFS theory and
application, while reviewing the methodology used and results obtained. This
includes the different approaches which were applied to both car and truck
models and a discussion of further alternatives to continue this research. In
addition, a thorough discussion on the depth of the work performed is
presented.
Keywords: LED, fuzzy, control, truck
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Capítulo 6
Conclusões e sugestões para trabalhos futuros
Apresentação
Neste capítulo são feitos alguns comentários finais, fornecendo uma
visão geral de todo o trabalho realizado, incluindo um paralelo entre as
diferentes abordagens feitas ao modelo proposto, tanto para os carros como
para os caminhões. Além disso, é feita uma lista de sugestões para a
continuação dos estudos e pesquisas desenvolvidos ao longo do trabalho,
envolvendo a exploração de novos aspectos relacionados ao processo, bem
como a investigação mais aprofundada de aspectos já explorados.
Palavras-chave: LED, fuzzy, controle, caminhão
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6.1 Comentários finais
Primeiramente é importante destacar a inovação apresentada neste
trabalho. O sistema proposto neste trabalho de AFS com LEDs para caminhões
pesados é inovador e possui uma série de vantagens em relação ao estado da
técnica atual. É possível destacar 4 pontos:
•
Campo de visão: o campo de visão do sistema incremental, em
comparação ao sistema com motor de passo, possibilita uma maior
amplitude no campo visual, da ordem de 30 graus para a direita e para a
esquerda, enquanto que o sistema com o motor de passo tem a
limitação de 15 graus para a direita e de 5 graus para esquerda quando
o tráfego é na mão direita. Nos mercados que trabalham com a mão
esquerda, os valores são invertidos;
•
Confiabilidade do sistema: devido ao fato do sistema eliminar a parte
móvel e conseqüentemente eliminar o motor acoplado ao refletor, a
probabilidade de mal funcionamento ou falha do componente dentro do
farol é reduzida. O sistema proposto possui a vantagem de ser
independente do farol principal, sem afetar o funcionamento do
componente;
•
Consumo elétrico: o modelo proposto, devido ao fato de adotar os LEDs,
possui um consumo menor em relação ao sistema com motor de passo,
pelo fato de possuir toda a eletrônica adicional para não só controlar o
mesmo, mas também para acionar o sistema quando necessário;
•
Aparência: a vantagem do modelo proposto permite uma aparência
diferenciada, criando uma diferenciação mercadológica e, além disso,
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não necessita de espaços adicionais ao redor do refletor nas
proximidades da moldura interna do farol para permitir a movimentação
do mesmo. Para o departamento de estilo de uma montadora, e também
para o consumidor final, não ser necessário a adição desse espaço
permite a criação de um design mais limpo e também inovador.
Além dos quatro pontos destacados acima, o trabalho desenvolvido
sugere a real necessidade de um modelo específico para controlar o AFS com
LEDs para o motorista de caminhão, a fim de proporcionar uma melhor
dirigibilidade noturna para o mesmo. Foi visto, ao longo do trabalho, que as
etapas da escolha da fonte luminosa, da lógica a ser adotada, das condições
de contorno, do meio ambiente e dos dados da pesquisa, foram, sem dúvida,
as peças-chaves para o surgimento deste modelo ajustado para o mercado
brasileiro.
No estudo comparativo entre os LEDs de alta eficiência e alta potência
da OSRAM e da Lumileds, levantado no capítulo 2, foram observadas algumas
diferenças no comportamento destes dispositivos. Os primeiros LEDs
estudados foram: Golden Dragon, K1 e K2. Para o K2 a curva de acomodação
entre o regime transiente e o regime estacionário só pode ser ajustada por
duas exponenciais (dois tempos de vida), enquanto as respostas do Golden
Dragon e do K1 podem ser ajustadas por uma única exponencial decrescente
para cada um dos LEDs. O Golden Dragon foi escolhido para esta aplicação.
Já o capítulo 3 descreveu o desenvolvimento e a aplicação de um
sistema baseado nas regras fuzzy que prescrevem e determinam a quantidade
de luz a ser adicionada ao farol durante o período de direção noturna. O
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modelo proposto apresenta a combinação entre o conhecimento do
especialista e um procedimento de estimação de parâmetros que permite o
ajuste de cada uma das regras de saída do modelo, considerando estruturas
diferentes de modelos fuzzy tipo 1 e tipo 2. Mais especificamente, o julgamento
no viés técnico do especialista pode ser apropriadamente incorporado ao
modelo de regras fuzzy de acordo com o seu conhecimento e a sua
experiência do período noturno e das condições do meio ambiente.
Alguns dos modelos são adequados, mas as duas melhores alternativas
são os modelos 7 e 8, que consideram a restrição que suporta a utilização do
conhecimento do especialista da melhor forma possível. Estes modelos têm
uma resposta similar ao especialista, devendo ser destacada a importância de
se criar uma direção noturna mais segura, provendo mais iluminação para o
motorista.
O capítulo 4 descreve as necessidades e o comportamento de ambos os
motoristas, de carro e caminhão, levando em consideração a iluminação das
ruas de São Paulo, no Brasil. O estudo gerou uma vasta quantidade de dados
que podem suportar o desenvolvimento de um sistema específico de AFS a
LEDs, levando em conta não só os dados da pesquisa, mas também as
condições das estradas e os resultados das simulações realizadas.
As informações dadas ao especialista ajudaram a definir a intensidade e
a distribuição de luz requerida quando o sistema AFS estiver disponível. É
importante destacar a necessidade de um modelo específico para o motorista
de caminhão. O modelo proposto confirma que o modelo de carro não pode ser
adotado pelo motorista de caminhão, pois não atende as suas necessidades.
Este estudo deixa claro que as necessidades dos motoristas de caminhão e de
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carro são diferentes, não somente pelo campo de visão e pelo tempo de
resposta, mas também pelos anseios em relação à iluminação veicular, que
são diferentes, conforme destacado na seção 4.3. Até o presente momento,
não existem estudos neste campo e este capítulo mostra que existem muitos
aspectos que podem ser aprofundados. Além disso, os dados coletados neste
estudo permitem o desenvolvimento de um sistema específico para os faróis
baixo e alto para o mercado brasileiro, para ambos carros e caminhões.
O modelo de AFS a LEDs para caminhões (conjunto de LEDs com ótica,
sistema de alimentação elétrica, um computador pessoal com lógica fuzzy
embarcada) para melhorar a dirigibilidade noturna foi desenvolvido levando em
consideração o conhecimento do especialista e a estimação dos parâmetros
que permitiu o ajuste do modelo de saída de cada regra, levando em
consideração diferentes estruturas fuzzy tipo 1 e tipo 2. Mais especificamente,
o julgamento do especialista pode ser incorporado na formação das regras
fuzzy de acordo com as condições das estradas e do meio ambiente. É
necessário enfatizar que a solução para o segmento de caminhões foi a
número 1, que demonstra uma superfície com menor variabilidade e utiliza
fuzzy do tipo 1, o que simplifica o modelo de saída. O modelo escolhido
confirma o conhecimento do especialista e claramente reduz a complexidade
do mecanismo global de controle para esta aplicação.
O capitulo 5 descreveu a parte experimental e a validação do protótipo
de bancada adotando o programa em VB (Visual Basic) e utilizou um PC para
simular o micro controlador. Um joystick com um sensor simulou o volante e o
sensor de posição do mesmo.
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Finalmente, como visto acima, destaca-se o fato de que cada capítulo
teve uma discussão que foi complementada pelo capítulo seguinte chegando à
geração do protótipo de bancada. O comparativo do modelo simulado versus o
modelo do protótipo validou o estudo e permitiu avançar para o estudo com os
dois fornecedores brasileiro e americano para desenvolver o produto veicular.
A patente para este modelo proposto está sendo requisitada, devido à sua
inovação no mercado brasileiro, já que esta seria a primeira vez desta
aplicação (AFS baseado em LEDs para caminhões) na indústria automotiva, e
iria gerar um grande avanço tecnológico no segmento de caminhões visto que
o estado da técnica atual adota apenas o sistema com motor de passo.
6.2 Sugestões para trabalhos futuros
São necessários estudos adicionais na parte experimental desta
pesquisa, mais especificamente para desenvolver o protótipo veicular do AFS a
LEDs com o modelo proposto neste trabalho para ser incorporado ao segmento
de caminhões, como um acessório ou um item opcional da montadora. Assim,
existe uma grande oportunidade de aprofundar o estudo no viés da aplicação
do modelo e validar o mesmo no campo através de um protótipo veicular.
Desta forma, será viável aperfeiçoar, ou não, o modelo que foi proposto, tanto
para o carro como para o caminhão. Isto já foi iniciado com discussões com os
fornecedores nos Estados Unidos e no Brasil.
Existem também oportunidades para desenvolvimentos adicionais em
relação ao sensor de posição, o que já esta sendo trabalhado pela equipe do
Laboratório de Propriedades Ópticas (LaPO).
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O recorte neste estudo também poderá ser ampliado na busca de uma
nova fonte de luminosidade (LED) com um rendimento melhor e um
desempenho superior em iluminação, visto que este mercado está em grande
ascensão.
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Apêndice A
Anteprojeto do relatório descritivo da patente de
invenção: farol auxiliar e processos relacionados
155
Appendix A
Draft of invention patent description report: headlight
help and related processes
156
A.1. Campo da invenção
A matéria aqui descrita pertence aos campos dos dispositivos de
iluminação veicular frontal.
A.2. Fundamentos da invenção
Dispositivos de iluminação veiculares frontais conhecidos atualmente
são faróis, faróis auxiliares, faróis de neblina, faróis de longo alcance, entre
outros. Os dispositivos conhecidos não possibilitam o incremento de iluminação
veicular gradativo, o que impede, por exemplo, um aumento no campo de visão
do motorista.
Documentos do estado da técnica definem sistema adaptativo de
iluminação veicular frontal como um dispositivo de iluminação que atua nas
luzes dianteiras de veículos, direcionando seu facho de luz, visando aumentar
a visibilidade dos motoristas em viagens noturnas, iluminando as faixas
divisórias das pistas e acostamentos, aumentando a probabilidade de evitar
acidentes. O funcionamento do sistema ocorre através de um motor de passo
que direciona a luz do farol para a direita e para a esquerda, num campo de 15
graus para a direita e 5 graus para esquerda em veículos que trafegam na mão
direita, auxiliando o motorista. Este sistema foi somente aplicado em veículos
de passeio.
Atualmente a fonte de energia utilizada para este tipo de aplicação são
lâmpadas halogenas ou de descarga de luz de gás xenônio.
Assim, é desejável que seja desenvolvido um dispositivo adaptativo de
iluminação veicular frontal que possa permitir o incremento de iluminação
veicular gradual, adicionando iluminação para o condutor do veiculo adotando o
uso de LEDs (diodos emissores de luz) como a fonte de energia para esta
aplicação.
A.3. Breve descrição da invenção
A matéria descrita no presente documento compreende um dispositivo
adaptativo de iluminação veicular frontal a LEDs que compreende um algoritmo
baseado em lógica fuzzy para controlar o acendimento gradual dos LEDs, de
157
acordo com as condições da via de rodagem. A invenção tem como objetivo:
1. Processo de desenvolvimento do algoritmo fuzzy para controlar o
sistema adaptativo de iluminação veicular dianteira a LEDs;
2. Meios para adaptar o algoritmo para qualquer mercado (brasileiro,
americano, europeu, asiático, entre outros);
3. Adotar este sistema em veículos pesados.
A.4. Breve descrição das figuras
A Figura A.1 mostra o diagrama esquemático de funcionamento do
sistema adaptativo de iluminação veicular a LEDs.
A Figura A.2 mostra a pesquisa de campo que foi realizada para
compreender as necessidades dos motoristas brasileiros tanto de carros como
de veículos pesados.
A Figura A.3 mostra uma alternativa para o farol auxiliar adaptativo com
o sistema de LEDs já incorporado ao farol principal de um veículo.
A Figura A.4 mostra a pista de testes que foi utilizada.
A.5. Descrição detalhada
Os exemplos aqui descritos são independentes entre si, e constituem
apenas exemplos possíveis de concretização sem contudo, limitar a matéria
aqui descrita.
Para efeitos desta invenção, considera-se como sistema adaptativo de
iluminação veicular auxiliar qualquer objeto que compreenda um dispositivo nas
luzes dianteiras de veículos direcionando seu facho de luz, visando aumentar a
visibilidade dos motoristas. Como exemplo, podem-se citar sistemas com motor
de passo em veículos de passeio que deslocam o bloco interno do farol para a
direita e para a esquerda, dependendo das condições de contorno.
A.5.1 Diagrama do sistema
O diagrama do sistema demonstra o funcionamento do sistema, que tem
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por principio a aquisição dos dados da velocidade e do raio da curvatura como
as variáveis de entrada do sistema para o controlador fuzzy. Este controlador
consiste em um modelo que determina a corrente de saída total que irá
alimentar os LEDs do sistema (Figura A.1).
Figura A.1: Diagrama de blocos do sistema.
A.5.2 Pesquisa de campo
Uma pesquisa de campo foi feita pra levantar as necessidades do
motorista levando em consideração uma série de fatores, como velocidade
empregada nas estradas, nas ruas ou no campo, o campo de visão, o alcance
desejável, entre outros. Isto serve de base para alimentar o algoritmo de lógica
fuzzy no que diz respeito às leis de pertinência do modelo. A Figura A.2
descreve em detalhes o questionário aplicado para coletar os dados em
questão.
159
Figura A.2: Detalhes do questionário aplicado para a coleta de dados.
160
A.5.3 Proposta do produto
Uma alternativa para a aplicação do sistema adaptativo de iluminação
veicular a LEDs pode ser apresentado na Figura A.3, onde existe um sistema
halogeno com o incremento dos LEDs de maneira sequencial.
Figura A.3: Sistema halogeno com o incremento dos LEDs de maneira
seqüencial.
A.5.4 Pista de testes
A Figura A.4 descreve em detalhes o campo de provas que foi utilizado
para levantar os dados de velocidade e raio de curvatura em diferentes
condições de dirigibilidade.
Figure A.4: Pista de testes.
161
A.6. Reivindicações
1. Unidade de farol veicular dianteiro auxiliar adaptativo alimentado por
LEDs de maneira sequencial controlado por um sistema de inferência
fuzzy, ficando acoplado ou não ao farol principal do veiculo de ambos
os lados do mesmo;
2. Unidade de farol veicular dianteiro auxiliar adaptativo alimentado por
LEDs de maneira sequencial controlado por um sistema de inferência
fuzzy, de acordo com a reivindicação 1, sendo este aplicado a
veículos pesados e de passeio;
3. Unidade de farol veicular dianteiro auxiliar adaptativo alimentado por
LEDs de maneira sequencial controlado por um sistema de inferência
fuzzy, de acordo com a reivindicação 1, seguindo o conceito de
identificação do modelo fuzzy através da coleta de informações com
pesquisa de campo (Figura A.2) e de dados de uma pista de testes
(Figura A.4);
4. Unidade de farol veicular dianteiro auxiliar adaptativo alimentado por
LEDs de maneira sequencial controlado por um sistema de inferência
fuzzy, de acordo com qualquer reivindicação de 1 a 3, que
compreende:
um sequenciamento de LEDs em qualquer parte do farol
principal dianteiro que adiciona ao facho principal uma
quantidade de luz que abrange a abertura lateral de 30 graus
para ambos os lados.
162
Apêndice B
Código computacional no MATLAB
163
Appendix B
MATLAB computer code
File “simu_tsk_pelo_principal.m”
function
y_s_la=simul_tsk_pelo_principal(Entrada,Saida,X_final,P1,P2,P3,P4,m,p,ind_mod,t_morto,val_pass,n_conjuntos_ent
rada,n_conjuntos_saida);
%FORMANDO AS MATRIZES C e S para CÁLCULO DA PREDIÇÃO SEGUNDO O
%MODELO TSK DO TIPO 2
k=1;
for i=1:m
for j=1:p+1
C(i,j)=X_final(k);
k=k+1;
end
end
for i=1:m
for j=1:p+1
S(i,j)=X_final(k);
%S(j)=X_final(k); % inserteza na entradae saida
k=k+1;
end
end
md_dados=[Entrada Saida];
%FAZENDO A JANELA DE TEMPO
[pdad,tdad]=pddin(t_morto,val_pass,md_dados);
%DEFININDO A MATRIZ DE DADOS PARA IDENTIFICACAO FUZZY
%A PRIMEIRA COLUNA CONTEM OS VALORES DA SAIDA DO MODELO FUZZY (y no
%instante k). A segunda coluna CONTEM OS VALORES DE y(k-1) e a terceira
%coluna em diante contem valores passados da entrada u.
mdados=[tdad'];
tam_pdad=size(pdad);
for t_pdad=tam_pdad(1):-1:1
mdados=[mdados pdad(t_pdad,:)'];
end
ndad=size(mdados,1);
ncol=size(mdados,2);
%SIMULAÇÃO DO MODELO
%PRIMEIRO VETOR DE ENTRADAS
p_val=max(max(max(t_morto,val_pass(length(val_pass)))),max(val_pass(1:length(val_pass)-1)));
p_valores=max(max(max(t_morto,val_pass(length(val_pass)))),max(val_pass(1:length(val_pass)-1))-1);
if p_valores==0
y_mod=md_dados(1,size(md_dados,2));
y_s_pf(1)=y_mod;
y_aux=0;
y_s_la(1)=y_aux;
p_valores=1;
164
else
for i=1:p_valores
y_mod=md_dados(i,size(md_dados,2));%PRIMEIROS VALORES DA SAIDA MEDIDA (UNICOS VALORES
DISPONÍVEIS PARA MODELO PREDITIVO)
y_aux=0;% PRIMEIROS VALORES DA SAIDA SIMULADA DESCONHECIDO (=ZERO)
y_s_la(i)=y_aux;
y_s_pf(i)=y_mod;
end
end
for i=1:ndad
%PREDIÇÃO DE LONGO ALCANCE
% VETOR COM AS ENTRADAS DO MODELO PARA PREDIÇÃO LONGO ALCANCE
x1=[y_s_la(i+p_valores-val_pass(length(val_pass)):i-1+p_valores) mdados(i,(2+val_pass(length(val_pass))):ncol)
1];
if x1==0
x1=x1+eps;
end
%SIMULANDO O TSK PARA PREDIÇÃO LONGO ALCANCE
[y_L,y_R,y_mod]=tsk_2(x1,P1,P2,P3,P4,C,S,ind_mod);
%GUARDANDO O VALOR PREDITO
y_s_la(i+p_val)=y_mod;
%ATUALIZANDO O VALOR DA SAIDA, PARA A PRÓXIMA ENTRADA,
%ATRIBUINDO A ÚLTIMA PREDIÇÃO DO MODELO (IDENTIFICACAO C/ LONGO ALCANCE)
y_aux=y_mod;
end
if size(y_s_la)==[1 length(y_s_la )]
y_s_la=y_s_la';
end
Erro_Fuzzy=var(y_s_la(p_valores:length(y_s_la)) - Saida(p_valores:length(Saida)));
load('dados_caminhao.mat', 'amperagem','velocidade','raio','tempo','latitude','longitude')
figure(3)
plot(latitude,longitude);
title('Simulation Test Track','FontWeight','bold','FontSize',18);
xlabel('Latitude','FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
ylabel('Longitude','FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
n_dec=200;
amperagem=amperagem(1:n_dec:length(amperagem));
velocidade=velocidade(1:n_dec:length(velocidade));
raio=raio(1:n_dec:length(raio));
tempo=tempo(1:n_dec:length(tempo));
figure(2);
plot(tempo,amperagem,'Marker','.','LineStyle','none',...
'Color','k');hold on;
plot(tempo(1:length(y_s_la)),y_s_la,'Marker','.','LineStyle','none',...
'Color','r');
% plot(tempo,raio,'Color',[0.6 0.6 0.6],'Marker','x','LineStyle','none');hold on;
% plot(tempo,velocidade,'b')
% set(gca,'YLim',[0 140])
set(gca,'XLim',[0 1000])
title('Specialist vs Model Simulation Data','FontWeight','bold','FontSize',18);
xlabel('Time[seconds]')
ax1 = gca;
% ylabel('Velocity [Km/h] ... Radius [m]','FontWeight','bold',...
% 'FontSize',12,...
% 'FontName','Arial');
165
% ax2 = axes('Position',get(ax1,'Position'),...
% 'YAxisLocation','right',...
% 'Color','none',...
% 'YColor','k');
% set(gca,'XLim',[0 1000])
% hl2 = line(tempo,amperagem,'Marker','.','LineStyle','none',...
% 'Color','k','Parent',ax2);hold on;
% hl3 = line(tempo(1:length(y_s_la)),y_s_la,'Marker','.','LineStyle','none',...
% 'Color','r','Parent',ax2);
% legend(ax1,'Radius [m]','Velocity [Km/h]','Location','South')
legend(ax1,'Specialist',['Model Fuzzy - Quadratic Error: ' num2str(Erro_Fuzzy,'%9.2f')],'Location','North')
set(get(ax1,'Ylabel'),'String','Current [mA]','VerticalAlignment','cap','FontWeight','bold',...
'FontSize',12,...
'FontName','Arial',...
'Color','k');
clear ax1 n_dec
figure(1)
n_disc=40;
malha=zeros(n_disc,1);
Teste1=linspace(0,200,n_disc)'; % velocidade
Teste2=linspace(0,200,n_disc)'; % raio
[xc,yc] = ndgrid(Teste1,Teste2);
for n_malha=1:n_disc
md_dados=[[xc(:,n_malha) yc(:,n_malha)] zeros(n_disc,1)];
%FAZENDO A JANELA DE TEMPO
[pdad,tdad]=pddin(t_morto,val_pass,md_dados);
%DEFININDO A MATRIZ DE DADOS PARA IDENTIFICACAO FUZZY
%A PRIMEIRA COLUNA CONTEM OS VALORES DA SAIDA DO MODELO FUZZY (y no
%instante k). A segunda coluna CONTEM OS VALORES DE y(k-1) e a terceira
%coluna em diante contem valores passados da entrada u.
mdados=[tdad'];
tam_pdad=size(pdad);
for t_pdad=tam_pdad(1):-1:1
mdados=[mdados pdad(t_pdad,:)'];
end
ndad=size(mdados,1);
ncol=size(mdados,2);
%SIMULAÇÃO DO MODELO
%PRIMEIRO VETOR DE ENTRADAS
p_val=max(max(max(t_morto,val_pass(length(val_pass)))),max(val_pass(1:length(val_pass)-1)));
p_valores=max(max(max(t_morto,val_pass(length(val_pass)))),max(val_pass(1:length(val_pass)-1))-1);
if p_valores==0
y_mod=md_dados(1,size(md_dados,2));
y_s_pf(1)=y_mod;
y_aux=0;
y_s_la_m(1)=y_aux;
p_valores=1;
else
for i=1:p_valores
y_mod=md_dados(i,size(md_dados,2));%PRIMEIROS VALORES DA SAIDA MEDIDA (UNICOS VALORES
DISPONÍVEIS PARA MODELO PREDITIVO)
y_aux=0;% PRIMEIROS VALORES DA SAIDA SIMULADA DESCONHECIDO (=ZERO)
y_s_la_m(i)=y_aux;
y_s_pf(i)=y_mod;
end
166
end
for i=1:ndad
%PREDIÇÃO DE LONGO ALCANCE
% VETOR COM AS ENTRADAS DO MODELO PARA PREDIÇÃO LONGO ALCANCE
x1=[y_s_la_m(i+p_valores-val_pass(length(val_pass)):i-1+p_valores)
mdados(i,(2+val_pass(length(val_pass))):ncol) 1];
if x1==0
x1=x1+eps;
end
%SIMULANDO O TSK PARA PREDIÇÃO LONGO ALCANCE
[y_L,y_R,y_mod]=tsk_2(x1,P1,P2,P3,P4,C,S,ind_mod);
%GUARDANDO O VALOR PREDITO
y_s_la_m(i+p_val)=y_mod;
%ATUALIZANDO O VALOR DA SAIDA, PARA A PRÓXIMA ENTRADA,
%ATRIBUINDO A ÚLTIMA PREDIÇÃO DO MODELO (IDENTIFICACAO C/ LONGO ALCANCE)
y_aux=y_mod;
end
if size(y_s_la_m)==[1 length(y_s_la_m )]
y_s_la_m=y_s_la_m';
end
y_s_la_m(1)=y_s_la_m(2);
if malha(:,1)==zeros(n_disc,1)
malha=y_s_la_m;
else
malha=[malha y_s_la_m];
end
end
surf(xc,yc,malha,'FaceColor','interp','FaceLighting','phong')
camlight right
view([-60 80]); %view([-45 80]);
set(gca,'Zlim',[0 2000],'CLim',[0 2000])
ylabel(' Radius [m]','FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
xlabel(' Velocity [Km/h] ','FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
return
167
File “GRAF_ESP.m”
load('dados_caminhao.mat', 'amperagem','velocidade','raio','tempo','latitude','longitude')
n_dec=200;
% amperagem=decimate(amperagem,n_dec,'fir');
% velocidade=decimate(velocidade,n_dec,'fir');
% raio=decimate(raio,n_dec,'fir');
% tempo=decimate(tempo,n_dec,'fir');
amperagem=amperagem(1:n_dec:length(amperagem));
velocidade=velocidade(1:n_dec:length(velocidade));
raio=raio(1:n_dec:length(raio));
tempo=tempo(1:n_dec:length(tempo));
figure(2);
plot(tempo,raio,'Color',[0.6 0.6 0.6],'Marker','x','LineStyle','none');hold on;
plot(tempo,velocidade,'b')
set(gca,'YLim',[0 140])
set(gca,'XLim',[0 1000])
title('Specialist Simulation Data','FontWeight','bold','FontSize',18);
xlabel('Time[seconds]')
ax1 = gca;
ylabel('Velocity [Km/h] ... Radius [m]','FontWeight','bold',...
'FontSize',12,...
'FontName','Arial');
ax2 = axes('Position',get(ax1,'Position'),...
'YAxisLocation','right',...
'Color','none',...
'YColor','k');
set(gca,'XLim',[0 1000])
hl2 = line(tempo,amperagem,'Marker','.','LineStyle','none',...
'Color','k','Parent',ax2);
legend(ax1,'Radius [m]','Velocity [Km/h]','Location','South')
set(get(ax2,'Ylabel'),'String','Current [mA]','VerticalAlignment','cap','FontWeight','bold',...
'FontSize',12,...
'FontName','Arial',...
'Color','k');
clear ax1 ax2 hl2 n_dec
168
File “func_ob.m”
%FUNCTION PARA DEFINIÇÃO DA FUNÇÃO OBJETIVO
%
function fob=func_ob(xp)
global alfa ind_mod ind_otim t_morto val_pass md_dados% Definidas na função tsk_po_t2
global P1 P2 P3 P4 m p % Definidas na função define_conj_fuzzy
global xp0 n_par ind_par mdados m_faixa n_par_f X_final % Definidas na função aprox_inicial
%DESNORMALIZANDO OS PARAMETROS A SEREM ESTIMADOS
for i=1:n_par_f
xpar(i)=(m_faixa(i,2)-m_faixa(i,1))*xp(i)+m_faixa(i,1);
end
%ATUALIZANDO VETOR COM TODOS OS PARÂMETROS (AJUSTADOS E FIXOS)
kk=1;jj=1;
for i=1:n_par % n_par é o número máximo de parâmetros que podem ser ajustados.
if ind_par(i)==0 %0-> Indica parâmetro fixo durante todo o problema de otimização.
x_full(i)=xp0(jj);%coloca cond. iniciais
jj=jj+1;
else
x_full(i)=xpar(kk); %coloca valores atualizados.
kk=kk+1;
jj=jj+1;
end
end
x_full'
%[x_full(3) x_full(15);x_full(6) x_full(18);x_full(9) x_full(21);x_full(12) x_full(24);]
%ind_otim=ind_otim+1
%FORMANDO AS MATRIZES C e S para CÁLCULO DA PREDIÇÃO SEGUNDO O
%MODELO TSK DO TIPO 2
k=1;
for i=1:m
for j=1:p+1
C(i,j)=x_full(k);
k=k+1;
end
end
for i=1:m
for j=1:p+1
S(i,j)=x_full(k);
% S(j)=x_full(k); % incerteza na entradae saida
k=k+1;
end
end
%
%FAZENDO A SIMULAÇÃO PARA A AMOSTRA DE DADOS
ndad=size(mdados,1);
ncol=size(mdados,2);
%PRIMEIRO VETOR DE ENTRADAS
p_val=max(max(max(t_morto,val_pass(length(val_pass)))),max(val_pass(1:length(val_pass)-1)));
p_valores=max(max(max(t_morto,val_pass(length(val_pass)))),max(val_pass(1:length(val_pass)-1))-1);
if p_valores==0
y_mod=md_dados(1,size(md_dados,2));
y_s_pf(1)=y_mod;
y_aux=0;
y_s_la(1)=y_aux;
169
p_valores=1;
else
for i=1:p_valores
y_mod=md_dados(i,size(md_dados,2));%PRIMEIROS VALORES DA SAIDA MEDIDA (UNICOS VALORES
DISPONÍVEIS PARA MODELO PREDITIVO)
y_aux=0;% PRIMEIROS VALORES DA SAIDA SIMULADA DESCONHECIDO (=ZERO)
y_s_la(i)=y_aux;
y_s_pf(i)=y_mod;
end
end
for i=1:ndad
if alfa~=0
%PREDIÇÃO DE LONGO ALCANCE
% VETOR COM AS ENTRADAS DO MODELO PARA PREDIÇÃO LONGO ALCANCE
x1=[y_s_la(i+p_valores-val_pass(length(val_pass)):i-1+p_valores) mdados(i,(2+val_pass(length(val_pass))):ncol)
1];
%SIMULANDO O TSK PARA PREDIÇÃO LONGO ALCANCE
if x1==0
x1=x1+eps;
end
[y_L,y_R,y_mod]=tsk_2(x1,P1,P2,P3,P4,C,S,ind_mod);
%GUARDANDO O VALOR PREDITO
y_s_la(i+p_val)=y_mod;
%ATUALIZANDO O VALOR DA SAIDA, PARA A PRÓXIMA ENTRADA,
%ATRIBUINDO A ÚLTIMA PREDIÇÃO DO MODELO (IDENTIFICACAO C/ LONGO ALCANCE)
y_aux=y_mod;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%
%PREDIÇÃO UM PASSO A FRENTE
% VETOR COM AS ENTRADAS DO MODELO PARA PREDIÇÃO UM PASSO A FRENTE
if alfa~=1
x2=[mdados(i,2:ncol) 1];
%SIMULANDO O TSK PARA PREDIÇÃO UM PASSO A FRENTE
if x2==0
x2=x2+eps;
end
[y_L,y_R,y_mod]=tsk_2(x2,P1,P2,P3,P4,C,S,ind_mod);
%GUARDANDO O VALOR PREDITO
y_s_pf(i+p_valores)=y_mod;
end
end
if alfa~=0
y_s_la=y_s_la';
end
if alfa~=1
y_s_pf=y_s_pf';
end
%CALCULANDO A FUNÇÃO OBJETIVO
if (alfa>0)&(alfa<1)
170
fob=alfa*sum((y_s_l-[md_dados(1:p_val,size(md_dados,2));mdados(:,1)]).^2)+(1-alfa)*sum((y_s_pf[md_dados(1:p_val,size(md_dados,2));mdados(:,1)]).^2);
if isnan(fob)
fob=1E100;
end
if fob>1E200
fob=1E100;
end
elseif alfa~=0
limite=0.90;fob=0.1*norm(y_s_lalimite*[md_dados(1:p_val,size(md_dados,2));mdados(:,1)])+0.9*norm(abs(y_s_lalimite*[md_dados(1:p_val,size(md_dados,2));mdados(:,1)])-(y_s_lalimite*[md_dados(1:p_val,size(md_dados,2));mdados(:,1)]))
%fob=norm(y_s_la-[md_dados(1:p_val,size(md_dados,2));mdados(:,1)])
if isnan(fob)
fob=1E100;
end
if fob>1E200
fob=1E100;
end
else
% fob=norm(y_s_pf-[md_dados(1:p_val,size(md_dados,2));mdados(:,1)])
if isnan(fob)
fob=1E100;
end
if fob>1E200
fob=1E100;
end
end
%PLOTANDO O RESULTADO (PREDIÇÃO DE LONGO ALCANCE) A CADA ITERAÇAO
if alfa~=0
figure(1)
plot([y_s_la [md_dados(1:p_val,size(md_dados,2));mdados(:,1)]]);
drawnow
%salva= ['saveas(gcf,''simula' num2str(ind_otim) '.jpg'', ''jpg'')'];
%eval(salva);
%salva= ['saveas(gcf,''simula' num2str(ind_otim) '.fig'', ''fig'')'];
%eval(salva);
%salva= ['save INTERACAO' num2str(ind_otim)];
%eval(salva);
end
if alfa~=1
figure(1)
plot([y_s_pf [md_dados(1:p_val,size(md_dados,2));mdados(:,1)]]);
drawnow
%salva= ['saveas(gcf,''simula' num2str(ind_otim) '.jpg'', ''jpg'')'];
%eval(salva);
%salva= ['saveas(gcf,''simula' num2str(ind_otim) '.fig'', ''fig'')'];
%eval(salva);
%salva= ['save INTERACAO' num2str(ind_otim)];
%eval(salva);
end
171
Apêndice C
Código computacional do protótipo
172
Appendix A
Prototype computer code
File “Project1.vbp”
Type=Exe
Form=Form1.frm
Reference=*\G{00020430-0000-0000-C000000000000046}#2.0#0#E:\WINDOWS\system32\stdole2.tlb#OLE Automation
Module=P_comunicacao; WinIo.Bas
Module=modInput; Joystick\modInput.bas
Module=C_interface; C_interface.bas
Module=fuzzy; fuzzy.bas
IconForm="Form1"
Startup="Form1"
ExeName32="Lukacs.exe"
Path32="C:\Documents and Settings\Lapo-06\Desktop"
Command32=""
Name="Project1"
HelpContextID="0"
CompatibleMode="0"
MajorVer=1
MinorVer=0
RevisionVer=0
173
AutoIncrementVer=0
ServerSupportFiles=0
VersionCompanyName="lapo"
CompilationType=0
OptimizationType=0
FavorPentiumPro(tm)=0
CodeViewDebugInfo=0
NoAliasing=0
BoundsCheck=0
OverflowCheck=0
FlPointCheck=0
FDIVCheck=0
UnroundedFP=0
StartMode=0
Unattended=0
Retained=0
ThreadPerObject=0
MaxNumberOfThreads=1
[MS Transaction Server]
AutoRefresh=1
174
File “Form1.frm”
Dim Ang As Long
Dim Vel As Long
Private Sub Sair_Click()
End
End Sub
Private Sub Form_Load()
Timer1.Interval = 1
End Sub
Private Sub regras()
led = 0
If Ang > 33000 Then
ld = calc_fuzzy((Ang - 32767) * 400 / 32768, (32767 - Vel) * 152 / 32767)
acender_leds (Direito + 2 ^ (ld - 1))
End If
If Ang < 32000 Then
ld = calc_fuzzy((32767 - Ang) * 400 / 32768, (32767 - Vel) * 152 / 32767)
acender_leds (Esquerdo + 2 ^ (ld - 1))
End If
If Ang < 33000 And Ang > 32000 Then acender_leds (31)
End Sub
Private Sub acender_leds(led As Integer)
175
' acender circulos na tela
d_led1.BackStyle = 0
d_led2.BackStyle = 0
d_led3.BackStyle = 0
d_led4.BackStyle = 0
d_led5.BackStyle = 0
e_led1.BackStyle = 0
e_led2.BackStyle = 0
e_led3.BackStyle = 0
e_led4.BackStyle = 0
e_led5.BackStyle = 0
' lado direito
If led = Direito + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 0: d_led3.BackStyle = 0
d_led4.BackStyle = 0: d_led5.BackStyle = 0
End If
If led = Direito + D1 + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 0
d_led4.BackStyle = 0: d_led5.BackStyle = 0
End If
If led = Direito + D2 + D1 + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 1
176
d_led4.BackStyle = 0: d_led5.BackStyle = 0
End If
If led = Direito + D3 + D2 + D1 + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 1
d_led4.BackStyle = 1: d_led5.BackStyle = 0
End If
If led = Direito + D4 + D3 + D2 + D1 + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 1
d_led4.BackStyle = 1: d_led5.BackStyle = 1
End If
' lado esquedo
If led = Esquerdo + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 0: d_led3.BackStyle = 0
d_led4.BackStyle = 0: d_led5.BackStyle = 0
End If
If led = Esquerdo + D1 + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 0
d_led4.BackStyle = 0: d_led5.BackStyle = 0
End If
If led = Esquerdo + D2 + D1 + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 1
d_led4.BackStyle = 0: d_led5.BackStyle = 0
End If
If led = Esquerdo + D3 + D2 + D1 + D0 Then
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d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 1
d_led4.BackStyle = 1: d_led5.BackStyle = 0
End If
If led = Esquerdo + D4 + D3 + D2 + D1 + D0 Then
d_led1.BackStyle = 1: d_led2.BackStyle = 1: d_led3.BackStyle = 1
d_led4.BackStyle = 1: d_led5.BackStyle = 1
End If
PortOut 888, led
End Sub
Private Sub Timer1_Timer()
' Carregando o estado do Joystick
PollJoystick
Text2 = CurrentJoyX
Ang = CurrentJoyX
Text3 = CurrentJoyY
Vel = CurrentJoyY
regras
Text2.Refresh
Text3.Refresh
End Sub
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File “C_interface.bas”
Public Const D0 = 1
Public Const D1 = 2
Public Const D2 = 4
Public Const D3 = 8
Public Const D4 = 16
Public Const Direito = 32
Public Const Esquerdo = 64
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File “fuzzy.bas”
Dim pnt(5000, 2) As Double
Private Function max_trimf(valo, a1, a2, a3)
If valo = a2 Then max_trimf = 1
If valo >= a1 And valo < a2 Then max_trimf = (1 / (a2 - a1) * valo + (1 - 1 / (a2 - a1) *
a2))
If valo > a2 And valo <= a3 Then max_trimf = (1 / (a2 - a3) * valo + (1 - 1 / (a2 - a3) *
a2))
End Function
Private Function max_trapmf(valo, a1, a2, a3, a4)
If valo >= a2 And valo <= a3 Then max_trapmf = 1
If valo >= a1 And valo < a2 Then max_trapmf = (1 / (a2 - a1) * valo + (1 - 1 / (a2 - a1) *
a2))
If valo > a3 And valo <= a4 Then max_trapmf = (1 / (a3 - a4) * valo + (1 - 1 / (a3 - a4) *
a3))
End Function
Private Function pnt_trapmf(valo, a1, a2, a3, a4, dis_xo, dis_xf)
' valo é o valoor maximo de y
For x = a1 To a4
If x >= a2 And x <= a3 Then fxi = 1
If x >= a1 And x < a2 Then fxi = (1 / (a2 - a1) * x + (1 - 1 / (a2 - a1) * a2))
If x > a3 And x <= a4 Then fxi = (1 / (a3 - a4) * x + (1 - 1 / (a3 - a4) * a2))
If fxi > valo Then fxi = valo
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pnt(x - dis_xo, 1) = x ' valoor x da função
If pnt(x - dis_xo, 2) < fxi Then pnt(x - dis_xo, 2) = fxi 'valoor y da função
'vs = vs + x * fxi
'vd = vd + fxi
Next x
'cen_trimf = vs / vd
End Function
Private Function pnt_trimf(valo, a1, a2, a3, dis_xo, dis_xf)
' valo é o valoor maximo de y
For x = a1 To a3
If x = a2 Then fxi = 1
If x >= a1 And x < a2 Then fxi = (1 / (a2 - a1) * x + (1 - 1 / (a2 - a1) * a2))
If x > a2 And x <= a3 Then fxi = (1 / (a2 - a3) * x + (1 - 1 / (a2 - a3) * a2))
If fxi > valo Then fxi = valo
pnt(x - dis_xo, 1) = x ' valoor x da função
If pnt(x - dis_xo, 2) < fxi Then pnt(x - dis_xo, 2) = fxi 'valoor y da função
'vs = vs + x * fxi
'vd = vd + fxi
Next x
'cen_trimf = vs / vd
End Function
Private Sub salvar()
Open "D:\dado.csv" For Output As #1
For i = 1 To 5000
Print #1, pnt(i, 1), ";", pnt(i, 2)
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Next i
Close #1
End Sub
Private Function cal_centroide(dis_xo, dis_xf) As Double
For i = 1 To dis_xf
ca = ca + pnt(i, 1) * pnt(i, 2)
cb = cb + pnt(i, 2)
Next i
cal_centroide = ca / cb
End Function
Public Function calc_fuzzy(raio, velocidade)
Dim valo(10, 3) As Double
Dim calc(10) As Double
valor1 = raio ' raio
valor2 = velocidade ' velocidade
' calculo da pertinencia para a primeira função
' raio pequeno
valo(1, 1) = max_trimf(valor1, 0, 20, 100)
' raio medio
valo(1, 2) = max_trapmf(valor1, 50, 120, 180, 280)
' raio grande
valo(1, 3) = max_trimf(valor1, 200, 395, 400)
' calculo da pertinencia para a segunda função
' velocidade baixa
valo(2, 1) = max_trimf(valor2, 0, 10, 50)
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' velocidade media
valo(2, 2) = max_trapmf(valor2, 40, 50, 70, 90)
' velocidade alta
valo(2, 3) = max_trimf(valor2, 71, 118, 152)
' apricando às funções de saida
'1
calc(1) = lowmodel(valo(1, 3), valo(2, 3))
pnt_trapmf calc(1), 6, 7, 13, 14, 0, 60
'2
calc(2) = lowmodel(valo(1, 3), valo(2, 2))
pnt_trapmf calc(2), 16, 17, 23, 24, 0, 60
calc(3) = lowmodel(valo(1, 2), valo(2, 3))
pnt_trapmf calc(3), 16, 17, 23, 24, 0, 60
'3
calc(4) = lowmodel(valo(1, 3), valo(2, 1))
pnt_trapmf calc(4), 26, 27, 23, 34, 0, 60
calc(5) = lowmodel(valo(1, 2), valo(2, 2))
pnt_trapmf calc(5), 26, 27, 23, 34, 0, 60
calc(6) = lowmodel(valo(1, 1), valo(2, 3))
pnt_trapmf calc(6), 26, 27, 23, 34, 0, 60
'4
calc(7) = lowmodel(valo(1, 2), valo(2, 1))
pnt_trapmf calc(7), 36, 37, 43, 44, 0, 60
calc(8) = lowmodel(valo(1, 1), valo(2, 2))
pnt_trapmf calc(8), 36, 37, 43, 44, 0, 60
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'
Debug.Print calc(7), calc(8)
'5
calc(9) = lowmodel(valo(1, 1), valo(2, 1))
pnt_trapmf calc(9), 46, 47, 53, 54, 0, 60
calc_fuzzy = cal_centroide(0, 60) / 10
'salvar
End Function
Private Function lowmodel(val1, val2) As Double
lowmodel = val1
If val1 > val2 Then lowmodel = val2
End Function
184
File “modInput.bas”
Option Explicit
Public Declare Function joyGetPosEx Lib "winmm.dll" (ByVal uJoyID As Long, pji As
JOYINFOEX) As Long
Public Declare Function joyGetDevCapsA Lib "winmm.dll" (ByVal uJoyID As Long, pjc As
JOYCAPS, ByVal cjc As Long) As Long
Public Type JOYCAPS
wMid As Integer
wPid As Integer
szPname As String * 32
wXmin As Long
wXmax As Long
wYmin As Long
wYmax As Long
wZmin As Long
wZmax As Long
wNumButtons As Long
wPeriodMin As Long
wPeriodMax As Long
wRmin As Long
wRmax As Long
wUmin As Long
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wUmax As Long
wVmin As Long
wVmax As Long
wCaps As Long
wMaxAxes As Long
wNumAxes As Long
wMaxButtons As Long
szRegKey As String * 32
szOEMVxD As String * 260
End Type
Public Type JOYINFOEX
dwSize As Long
dwFlags As Long
dwXpos As Long
dwYpos As Long
dwZpos As Long
dwRpos As Long
dwUpos As Long
dwVpos As Long
dwButtons As Long
dwButtonNumber As Long
dwPOV As Long
dwReserved1 As Long
dwReserved2 As Long
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End Type
Public JoyNum As Long
Public MYJOYEX As JOYINFOEX
Public MYJOYCAPS As JOYCAPS
Public CenterX As Long
Public CenterY As Long
Public JoyButtons(15) As Boolean
Public CurrentJoyX As Long
Public CurrentJoyY As Long
Public Function StartJoystick(Optional ByVal JoystickNumber As Long = 0) As Boolean
JoyNum = JoystickNumber
If joyGetDevCapsA(JoyNum, MYJOYCAPS, 404) <> 0 Then 'Get joystick info
StartJoystick = False
Else
MYJOYEX.dwSize = 64
MYJOYEX.dwFlags = 255
Call joyGetPosEx(JoyNum, MYJOYEX)
CenterX = MYJOYEX.dwXpos
CenterY = MYJOYEX.dwYpos
StartJoystick = True
End If
End Function
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Public Sub PollJoystick()
Dim i As Long
Dim t As Long
MYJOYEX.dwSize = 64
MYJOYEX.dwFlags = 255
' Get the joystick information
Call joyGetPosEx(JoyNum, MYJOYEX)
t = MYJOYEX.dwButtons
For i = 15 To 0 Step -1
JoyButtons(i) = False
If (2 ^ i) <= t Then
t = t - (2 ^ i)
JoyButtons(i) = True
End If
Next i
CurrentJoyX = MYJOYEX.dwXpos
CurrentJoyY = MYJOYEX.dwYpos
End Sub
188
File “P_comunicacao.bas”
Public Declare Sub PortOut Lib "io.dll" (ByVal Port As Integer, ByVal Value As Byte)
Public Declare Function PortIn Lib "io.dll" (ByVal Port As Integer) As Byte
Public Declare Sub Sleep Lib "kernel32" (ByVal dwMilliseconds As Long)
Sub vbOut(ByVal Port As Integer, ByVal Value As Byte)
PortOut Port, Value
End Sub
Function vbIn(ByVal Port As Integer)
vbIn = PortIn(Port)
End Function
189
Apêndice D
Carta de intenção do desenvolvimento do produto
190
Appendix D
Intention letter to develop the product
191
D.1. Carta da “De Ameretek Corporation”
192
D.2. Images of the proposed vehicle level prototype
Figure D.1: Prototype construction view.
193
Figure D.2: Prototype top view.
194
Figure D.3: Prototype front view.
195
Figure D.4: Prototype side view.
196