Computational Intelligence in Automotive Applications (Studies in Computational Intelligence, Volume 132)

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<ul><li><p>Danil Prokhorov (Ed.)</p><p>Computational Intelligence in Automotive Applications</p></li><li><p>Studies in Computational Intelligence, Volume 132</p><p>Editor-in-chiefProf. Janusz KacprzykSystems Research InstitutePolish Academy of Sciencesul. Newelska 601-447 WarsawPolandE-mail: kacprzyk@ibspan.waw.pl</p><p>Further volumes of this series can be found on ourhomepage: springer.com</p><p>Vol. 111. David Elmakias (Ed.)New Computational Methods in Power System Reliability,2008ISBN 978-3-540-77810-3</p><p>Vol. 112. Edgar N. Sanchez, Alma Y. Alans and AlexanderG. LoukianovDiscrete-Time High Order Neural Control: Trained withKalman Filtering, 2008ISBN 978-3-540-78288-9</p><p>Vol. 113. Gemma Bel-Enguix, M. Dolores Jimenez-Lopez andCarlos Martn-Vide (Eds.)New Developments in Formal Languages and Applications,2008ISBN 978-3-540-78290-2</p><p>Vol. 114. Christian Blum, Maria Jose Blesa Aguilera, AndreaRoli and Michael Sampels (Eds.)Hybrid Metaheuristics, 2008ISBN 978-3-540-78294-0</p><p>Vol. 115. John Fulcher and Lakhmi C. Jain (Eds.)Computational Intelligence: A Compendium, 2008ISBN 978-3-540-78292-6</p><p>Vol. 116. Ying Liu, Aixin Sun, Han Tong Loh, Wen Feng Luand Ee-Peng Lim (Eds.)Advances of Computational Intelligence in IndustrialSystems, 2008ISBN 978-3-540-78296-4</p><p>Vol. 117. Da Ruan, Frank Hardeman and Klaas van der Meer(Eds.)Intelligent Decision and Policy Making Support Systems,2008ISBN 978-3-540-78306-0</p><p>Vol. 118. Tsau Young Lin, Ying Xie, Anita Wasilewskaand Churn-Jung Liau (Eds.)Data Mining: Foundations and Practice, 2008ISBN 978-3-540-78487-6</p><p>Vol. 119. Slawomir Wiak, Andrzej Krawczyk and Ivo Dolezel(Eds.)Intelligent Computer Techniques in AppliedElectromagnetics, 2008ISBN 978-3-540-78489-0</p><p>Vol. 120. George A. Tsihrintzis and Lakhmi C. Jain (Eds.)Multimedia Interactive Services in Intelligent Environments,2008ISBN 978-3-540-78491-3</p><p>Vol. 121. Nadia Nedjah, Leandro dos Santos Coelhoand Luiza de Macedo Mourelle (Eds.)Quantum Inspired Intelligent Systems, 2008ISBN 978-3-540-78531-6</p><p>Vol. 122. Tomasz G. Smolinski, Mariofanna G. Milanovaand Aboul-Ella Hassanien (Eds.)Applications of Computational Intelligence in Biology, 2008ISBN 978-3-540-78533-0</p><p>Vol. 123. Shuichi Iwata, Yukio Ohsawa, Shusaku Tsumoto,Ning Zhong, Yong Shi and Lorenzo Magnani (Eds.)Communications and Discoveries from MultidisciplinaryData, 2008ISBN 978-3-540-78732-7</p><p>Vol. 124. Ricardo Zavala Yoe (Ed.)Modelling and Control of Dynamical Systems: NumericalImplementation in a Behavioral Framework, 2008ISBN 978-3-540-78734-1</p><p>Vol. 125. Larry Bull, Bernado-Mansilla Esterand John Holmes (Eds.)Learning Classifier Systems in Data Mining, 2008ISBN 978-3-540-78978-9</p><p>Vol. 126. Oleg Okun and Giorgio Valentini (Eds.)Supervised and Unsupervised Ensemble Methodsand their Applications, 2008ISBN 978-3-540-78980-2</p><p>Vol. 127. Regie Gras, Einoshin Suzuki, Fabrice Guilletand Filippo Spagnolo (Eds.)Statistical Implicative Analysis, 2008ISBN 978-3-540-78982-6</p><p>Vol. 128. Fatos Xhafa and Ajith Abraham (Eds.)Metaheuristics for Scheduling in Industrialand Manufacturing Applications, 2008ISBN 978-3-540-78984-0</p><p>Vol. 129. Natalio Krasnogor, Giuseppe Nicosia,Mario Pavone and David Pelta (Eds.)Nature Inspired Cooperative Strategies for Optimization(NICSO 2007), 2008ISBN 978-3-540-78986-4</p><p>Vol. 130. Richi Nayak, N. Ichalkaranje and Lakhmi C. Jain(Eds.)Evolution of Web in Artificial Intelligence Environments, 2008ISBN 978-3-540-79139-3</p><p>Vol. 131. Roger Lee and Haeng-Kon Kim (Eds.)Computer and Information Science, 2008ISBN 978-3-540-79186-7</p><p>Vol. 132. Danil Prokhorov (Ed.)Computational Intelligence in Automotive Applications, 2008ISBN 978-3-540-79256-7</p></li><li><p>Danil Prokhorov(Ed.)</p><p>Computational Intelligencein Automotive Applications</p><p>With 157 Figures and 48 Tables</p><p>123</p></li><li><p>Danil ProkhorovToyota Technical Center - A Divisionof Toyota Motor Engineeringand Manufacturing (TEMA)Ann Arbor, MI 48105USA</p><p>dvprokhorov@gmail.com</p><p>ISBN 978-3-540-79256-7 e-ISBN 978-3-540-79257-4</p><p>Studies in Computational Intelligence ISSN 1860-949X</p><p>Library of Congress Control Number: 2008925554</p><p>c 2008 Springer-Verlag Berlin Heidelberg</p><p>Thiswork is subject to copyright. All rights are reserved, whether thewhole or part of thematerial is con-cerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publica-tion or parts thereof is permitted only under the provisions of the German Copyright Law of September9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag.Violations are liable to prosecution under the German Copyright Law.</p><p>The use of general descriptive names, registered names, trademarks, etc. in this publication does notimply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.</p><p>Cover design: Deblik, Berlin, Germany</p><p>Printed on acid-free paper</p><p>9 8 7 6 5 4 3 2 1</p><p>springer.com</p></li><li><p>Computational Intelligence in Automotive Applications</p></li><li><p>Preface</p><p>What is computational intelligence (CI)? Traditionally, CI is understood as a collection of methods from thefields of neural networks (NN), fuzzy logic and evolutionary computation. Various definitions and opinionsexist, but what belongs to CI is still being debated; see, e.g., [13]. More recently there has been a proposalto define the CI not in terms of the tools but in terms of challenging problems to be solved [4].</p><p>With this edited volume I have made an attempt to give a representative sample of contemporary CIactivities in automotive applications to illustrate the state of the art. While CI research and achievements insome specialized fields described (see, e.g., [5, 6]), this is the first volume of its kind dedicated to automotivetechnology. As if reflecting the general lack of consensus on what constitutes the field of CI, this volumeillustrates automotive applications of not only neural and fuzzy computations1 which are considered to bethe standard CI topics, but also others, such as decision trees, graphical models, Support Vector Machines(SVM), multi-agent systems, etc.</p><p>This book is neither an introductory text, nor a comprehensive overview of all CI research in this area.Hopefully, as a broad and representative sample of CI activities in automotive applications, it will be worthreading for both professionals and students. When the details appear insufficient, the reader is encouragedto consult other relevant sources provided by the chapter authors.</p><p>The chapter Learning-based driver workload estimation discusses research on estimation of drivercognitive workload and proposes a new methodology to design driver workload estimation systems. Themethodology is based on decision-tree learning. It derives optimized models to assess the time-varying work-load level from data which include not only measurements from various sensors but also subjective workloadlevel ratings.</p><p>The chapter Visual monitoring of driver inattention introduces a prototype computer vision systemfor real-time detection of driver fatigue. The system includes an image acquisition module with an infraredilluminator, pupil detection and tracking module, and algorithms for detecting appropriate visual behaviorsand monitoring six parameters which may characterize the fatigue level of a driver. To increase effectivenessof monitoring, a fuzzy classifier is implemented to fuse all these parameters into a single gauge of driverinattentiveness. The system tested on real data from different drivers operates with high accuracy androbustly at night.</p><p>The chapter Understanding driving activity using ensemble methods complements the chapter Visualmonitoring of driver inattention by discussing whether driver inattention can be detected without eye andhead tracking systems. Instead of limiting themselves to working with just a few signals from preselectedsensors, the authors chose to operate on hundreds of signals reflecting the real-time environment both outsideand inside the vehicle. The discovery of relationships in the data useful for driver activity classification, as</p><p>1 Another standard CI topic called evolutionary computation (EC) is not represented in this volume in the formof a separate chapter, although some EC elements are mentioned or referenced throughout the book. Relevantpublications on EC for automotive applications are available (e.g., [7]), but unfortunately were not available ascontributors of this volume.</p></li><li><p>VIII Preface</p><p>well as ranking signals in terms of their importance for classification, is entrusted to an approach calledrandom forest, which turned out to be more effective than either hidden Markov models or SVM.</p><p>The chapter Computer vision and machine learning for enhancing pedestrian safety overviews methodsfor pedestrian detection, which use information from on-board and infrastructure based-sensors. Many ofthe discussed methods are sufficiently generic to be useful for object detection, classification and motionprediction in general.</p><p>The chapter Application of graphical models in the automotive industry describes briefly how graphicalmodels, such as Bayesian and Markov networks, are used at Volkswagen and Daimler. Production planningat Volkswagen and demand prediction benefit significantly from the graphical model based system developed.Another data mining system is developed for Daimler to help assessing the quality of vehicles and identifyingcauses of troubles when the vehicles have already spent some time in service. It should be noted that otherautomotive companies are also pursuing data mining research (see, e.g., [8]).</p><p>The chapter Extraction of maximum support rules for the root cause analysis discusses extraction ofrules from manufacturing data for root cause analysis and process optimization. An alternative approach totraditional methods of root cause analysis is proposed. This new approach employs branch-and-bound princi-ples, and it associates process parameters with results of measurements, which is helpful in the identificationof the main drivers for quality variations of an automotive manufacturing process.</p><p>The chapter Neural networks in automotive applications provides an overview of neural network tech-nology, concentrating on three main roles of neural networks: models, virtual or soft sensors and controllers.Training of NN is also discussed, followed by a simple example illustrating the importance of recurrent NN.</p><p>The chapter On learning machines for engine control deals with modeling for control of turbochargedspark ignition engines with variable camshaft timing. Two examples are considered: (1) estimation of thein-cylinder air mass in which open loop neural estimators are combined with a dynamic polytopic observer,and (2) modeling an in-cylinder residual gas fraction by a linear programming support vector regressionmethod. The authors argue that models based on first principles (white boxes) and neural or other blackbox models must be combined and utilized in the grey box approach to obtain results which are not justsuperior to any alternatives but are also more acceptable to automotive engineers.</p><p>The chapter Recurrent neural networks for AFR estimation and control in spark ignition automotiveengines complements the chapter On learning machines for engine control by discussing specifics of theair-fuel ratio (AFR) control. Recurrent NN are trained off-line and employed as both the AFR virtual sensorand the inverse model controller. The authors also provide a comparison with a conventional control strategyon a real engine.</p><p>The chapter Intelligent vehicle power management: An overview presents four case studies: a conven-tional vehicle power controller and three different approaches for a parallel HEV power controller. Theyinclude controllers based on dynamic programming and neural networks, and fuzzy logic controllers, one ofwhich incorporates predictions of driving environments and driving patterns.</p><p>The chapter Integrated diagnostic process for automotive systems provides an overview of model-basedand data-driven diagnostic methods applicable to complex systems. Selected methods are applied to threeautomotive examples, one of them being a hardware-in-the-loop system, in which the methods are put towork together to solve diagnostic and prognostic problems. It should be noted that integration of differentapproaches is an important theme for automotive research spanning the entire product life cycle (see, e.g.,[9]).</p><p>The chapter Automotive manufacturing: intelligent resistance welding introduces a real-time controlsystem for resistance spot welding. The control system is built on the basis of neural networks and fuzzylogic. It includes a learning vector quantization NN for assessing the quality of weld nuggets and a fuzzylogic process controller. Experimental results indicate substantial quality improvement over a conventionalcontroller.</p><p>The chapter Intelligent control of mobility systems (ICMS) overviews projects of the ICMS Programat the National Institute of Standards and Technology (NIST). The program provides architecture, interfaceand data standards, performance test methods and infrastructure technology available to the manufacturingindustry and government agencies in developing and applying intelligent control technology to mobilitysystems. A common theme among these projects is autonomy and the four dimensional/real-time control</p></li><li><p>Preface IX</p><p>systems (4D/RCS) control architecture for intelligent systems proposed and developed in the NIST IntelligentSystems Division.</p><p>Unlike the books index, each chapter has its own bibliography for the convenience of the reader, withlittle overlap among references of different chapters.</p><p>This volume highlights important challenges facing CI in the automotive domain. Better vehicle diag-nostics/vehicle system safety, improved control of vehicular systems and manufacturing processes to saveresources and minimize impact on the environment, better driver state monitoring, improved safety of pedes-trians, making vehicles more intelligent on the road these are important directions where the CI technologycan and should make the impact. All of these are consistent with the Toyota vision [10]:</p><p>Toyotas vision is to balance Zeronize and Maximize. Zeronize symbolizes the vision and philosophyof our persistent efforts in minimizing negative aspects vehicles have such as environmental impact, trafficcongestion and traffic accidents, while Maximize symbolizes the vision and philosophy of our persistentefforts in maximizing the positive aspects vehicles have such as fun, delight, excitement and comfort, thatpeople seek in automobiles.</p><p>I am very thankful to all the contributors of this edited volume for their willingness to participate in thisproject, their patience and valuable time. I am also grateful to Prof. Janusz Kacprzyk, the Springer SeriesEditor, for his encouragement to organize and edit this volume, as well as Thomas Ditzinger, the Springerproduction editor for h...</p></li></ul>

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