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Intelligent systems in the automotive industry: applications and trends

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Abstract

There is a common misconception that the automobile industry is slow to adapt new technologies, such as artificial intelligence (AI) and soft computing. The reality is that many new technologies are deployed and brought to the public through the vehicles that they drive. This paper provides an overview and a sampling of many of the ways that the automotive industry has utilized AI, soft computing and other intelligent system technologies in such diverse domains like manufacturing, diagnostics, on-board systems, warranty analysis and design.

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References

  1. Aston Martin DB9 Brochure (2005) http://www.astonmartin.com/thecars/db9

  2. Bajpai A, Richard M (1989) Charley: an expert system for diagnostics of manufacturing equipment. In: Schorr H, Rappaport A (eds) Proceedings of the first annual conference on innovative applications of artificial intelligence, AAAI Press, Menlo Park, CA, pp 178–185

    Google Scholar 

  3. Bishop R (2005) Intelligent vehicle technology and trends. Artech House, Norwood, MA

    Google Scholar 

  4. Boverie S, Demaya B, Le Quellec JM et al (1993) Contribution of fuzzy logic control to the improvement of modern car performances. Control Eng Practice 1(2):291–297

    Article  Google Scholar 

  5. Breed D (2004) Telematics system for vehicle diagnostics.

  6. Brown D, Leal D, McMahon C et al (2004) A Web-enabled virtual repository for supporting distributed automotive component development. Adv Eng Inform 18:173–190

    Article  Google Scholar 

  7. Buhler D, Vignier S, Heisterkamp P et al (2003) Safety and operating issues for mobile human–machine interfaces. In: Proceedings of the IUI'03, Miami, FL

  8. Bussman S, Schild K (2000) Self-organizing manufacturing control: an industrial application of agent technology. In: Proceedings of the 4th international conference on multi-agent systems, Boston, MA, pp 87–94

  9. Campos M, Bonabeau E, Theraulaz G et al (2001) Dynamic scheduling and division of labor in social insects. Adapt Behav 8(2):83–92

    Article  Google Scholar 

  10. Cavaretta M, Chou G, Madani B (2005) Using data mining to improve supplier release stability. In: Proceedings of the North American fuzzy information processing society (NAFIPS-2005) annual conference: soft computing for real world applications, Ann Arbor, MI, pp 252–256

  11. Cheng J, Lu Y, Puskorius G et al (1999) Vehicle sequencing based on evolutionary computation. In: Proceedings of IEEE congress on evolutionary computation, Washington, DC, pp 1207–1214

  12. Clark A, Filev D (2004) Intelligent agent for manufacturing rule generation. In: Proceedings of the ACM thirteenth conference in information and knowledge management, Washington DC, pp 495–500

  13. Clark A, Filev D (2005) Clustering techniques for rule extraction from unstructured text fragments. In: Proceedings of the North American fuzzy information processing society annual conference, Ann Arbor, MI, pp 793–798

  14. Craenen B, Eiben A (2003) Computational intelligence. In: Encyclopedia of life support sciences, EOLSS, London, UK

    Google Scholar 

  15. Cunningham A, Smart R (1993) Computer aided parts estimation. In: Proceedings of the fifth innovative applications of artificial intelligence, AAAI Press, Washington, DC, pp 14–25

    Google Scholar 

  16. Djurdjanovic D, Lee J, Ni J (2003) Watchdog agent – an infotronics-based prognostics approach for product performance degradation assessment and prediction. Adv Eng Inform 17:109–125

    Article  Google Scholar 

  17. Engstrom J, Victor T (2005) System and method for real-time recognition of driving patterns. US Patent Application 20,050,159,851

  18. Everson M, Mangin C, Stumpo C et al (2002) Evaluating strategies for foreign exchange risk reduction. In: Proceedings of the 2002 congress on evolutionary computation. IEEE Press, Honolulu, HI, pp 735–740

    Chapter  Google Scholar 

  19. Filev D (1997) Diagnosing from descriptive knowledge bases. In: Proceedings of the NAFIPS annual meeting, Syracuse, NY, pp 440–443

  20. Filev D (2002) Applied intelligent control—control of automotive paint process. In: Proceedings of the 2002 IEEE congress on fuzzy systems. IEEE Press, Honolulu, HI, pp 1–6

    Chapter  Google Scholar 

  21. Filev D, Huff J, Nagisetti I et al (1996) Phosphate bath control system. In: Proceedings of the IBEC'96, Detroit, MI, pp 6–10

  22. Friel P, Mayer R, Lockledge J et al (1989) Coolsys: a cooling system design assistant. In: Proceedings of the first annual conference on innovative applications of artificial intelligence, AAAI Press, Menlo Park, CA, pp 203–212

    Google Scholar 

  23. Greitzer FL, Pawlowski RA (2002) Embedded prognostics health monitoring. In: 48th Annual instrumentations, systems, and automation society international instrumentation symposium, San Diego, CA

  24. Greitzer FL, Hostick CJ, Rhoads RE et al (2001) Determining how to do prognostics, and then determining what to do with it. In: Proceedings of the AUTOTESTCON, Valley Forge, PA

  25. Gupta M, Tu M, Khan L, Bastani F, Yen I-L (2005) A study of the model and algorithms for handling location-dependent continuous queries. Know Inf Syst 8(4):414–437

    Article  Google Scholar 

  26. Gusikhin OY, Lewis DT, Miteff JR (1996) Integration of plant floor information for scheduling and control. SAE Trans 105:975–981

    Google Scholar 

  27. Gusikhin O, Klampfl E, Rossi G et al (2003) e-Workcell: a virtual reality Web-based decision support system for assembly line Planning. In: Piattini M, Filipe J, Braz J (eds) Enterprise information systems IV. Kluwer, Hingham, MA, pp 4–10

    Google Scholar 

  28. Hadden G, Bergstrom P, Bennett B et al (1999) Shipboard machinery diagnostics and prognostics/condition based maintenance: a progress report. In: Proceedings of the maintenance and reliability conference, MARCON'99, Gatlinburg, TN, pp 73.01–73.16

  29. Harper AP (1999) Knowledge-based engineering. Eng Des pp 29–33

  30. Heisterkamp P (2001) Linguatronic: product-level speech system for Mercedes-Benz car. In: Proceedings of the first international conference on human language technology research (HLT). Kaufmann, San Francisco, CA

    Google Scholar 

  31. Hotz E, Nakhaeizadeh G, Petzsche B et al (1999) WAPS, a data mining support environment for the planning of warranty and goodwill costs in the automotive industry. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, pp 417–419

  32. Hotz E, Grimmer U, Heuser W et al (2001) Revi-miner, a KDD-environment for deviation detection and analysis of warranty and goodwill cost statements in automotive industry. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, pp 432–437

  33. Hotz E, Grimmer U, Nakhaeizadeh G (2002) Some recent KDD-applications at daimlerChrysler AG. In: Schubert S, Reusch B, Jesse N (eds) Proceedings zur informatik 2002, Dortmund, Germany. Lect Notes Inform P-19:805–810

  34. Ishihara S, Ishihara K, Nagamachi M et al (1995) An automatic builder for a kansei engineering expert system using self-organizing neural metworks. Int J Ind Ergon 15:13–24

    Article  Google Scholar 

  35. Kurzweil R (1990) The age of intelligent machines, MIT Press, Cambridge, MA

    Google Scholar 

  36. Luo J, Namburu M, Pattipati K et al (2003b) Model-based prognostic techniques (maintenance applications). In: Proceedings of AUTOTESTCON, IEEE systems readiness technology conference, Anaheim, CA, pp 330–340

  37. Luo J, Tu F, Azam M et al (2003a) Intelligent model-based diagnostics for vehicle health management. Proc SPIE 5107:13–26

    Article  Google Scholar 

  38. Maier A, Schnurr H, Sure Y (2003) Ontology-based information integration in the automotive industry. In: Proceedings of the 2nd international semantic web conference, Sanibel Island, FL

  39. Marko K, James V, Feldkamp T et al (1996b) Training recurrent networks for classification: realization of automotive engine diagnostics. In: Proceedings of the world congress on neural networks (WCNN'96), San Diego, CA, pp 845–850

  40. Marko K, James J, Feldkamp T et al (1996a) Signal processing by neural networks to create virtual sensors and model-based diagnostics. In: Proceedings of the 1996 international conference on artificial neural networks, vol 1112, Springer, Berlin Heidelberg New York, pp 191–196

    Google Scholar 

  41. Matsubara Y, Nagamachi M, Jindo T (1994) Kansei engineering as an artificial intelligence system. In: Human factors in organizational design and management-IV, Elsevier, Amsterdam, The Netherlands, pp 473–478

    Google Scholar 

  42. Minker W, Haiber U, Heisterkamp P et al (2002) Intelligent dialogue strategy for accessing infotainment applications in mobile environments. In: Proceedings of the ISCA tutorial and research workshop on multi-modal dialogue in mobile environments, Kloster Irsee, Germany

    Google Scholar 

  43. Miyahara S, Sielagoski J, Koulinitch A et al (2006) Target tracking by a single camera based on range window algorithm and pattern matching. In: Proceedings of the 2006 SAE world congress, Detroit, MI

  44. Morgan A, Cafeo J, Godden K et al (2005) The general motors variation-reduction adviser: deployment issues for an AI application. AI Mag 26(3):19–28

    Google Scholar 

  45. Morley R (1996) Painting trucks at general motors: the effectiveness of a complexity-based approach. In: Embracing complexity: exploring the application of complex adaptive systems to business, Ernst and Young Center for Business Innovation, Cambridge, MA, pp 53–58

    Google Scholar 

  46. Morley R, Ekberg G (1998) Cases in chaos: complexity-based approaches to manufacturing. In: Embracing complexity: a colloquium on the application of complex adaptive systems to business, Ernst and Young Center for Business Innovation, Cambridge, MA, pp 97–102

    Google Scholar 

  47. Nadel BA, Wu X, Kagan D (1993) Multiple abstraction levels in automobile transmission design: constraint satisfaction formulations and implementations. Int J Expert Syst: Res Appl 6(4):489–559

    Google Scholar 

  48. Nagamachi M (2002) Kansei engineering as a powerful consumer-oriented technology for product development. Appl Ergon 33:289–294

    Article  Google Scholar 

  49. Nigro JM, Rombaut M (2003) IDRES: A rule-based system for driving situation recognition with uncertainty management. Inf Fus 4:309–317

    Article  Google Scholar 

  50. Pechoucek M, Rehak M, Marik V (2005) Expectations and deployment of agent technology in manufacturing and defence: case studies. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems, The Netherlands, pp 100–106

  51. Pieraccini R, Dayanidhi K, Bloom J et al (2004) Multimodal conversational systems for automobiles. Commun ACM (CACM) 47(1):47–49

    Article  Google Scholar 

  52. Priddle A (2005) Data mining key to reducing warranty costs, IBM says. Wards Auto Week

  53. Puskorius G, Feldkamp LA (2001) Parameter-based kalman filter training: theory and implementation. In: Haykin S (ed) Kalman filtering and neural networks, Wiley, New York

    Google Scholar 

  54. Rychtyckyj N (1999) DLMS: ten years of AI for vehicle assembly process planning. In: Proceedings of the AAAI-99/IAAI-99, AAAI Press, Orlando, FL, pp 821–828

    Google Scholar 

  55. Rychtyckyj N (2002) An assessment of machine translation for vehicle assembly process planning at Ford Motor Company. Machine translation: from research to real users. In: Proceedings of the 5th conference of the Association for Machine Translation in the Americas (AMTA 2002), Tiburon, CA, Springer, Berlin Heidelberg New York, pp 207–215

    Google Scholar 

  56. Rychtyckyj N (2005) Ergonomics analysis for vehicle assembly using artificial intelligence. AI Mag 26(3):41–50

    Google Scholar 

  57. Sanseverino M, Cascio F (1997) Model-based diagnosis for automotive repair. IEEE Intell Syst 12:33–37

    Google Scholar 

  58. Schouten NJ, Salman MA, Kheir NA (2003) Fuzzy logic control for parallel hybrid vehciles. IEEE Trans Control Syst Technol 10(3):460–468

    Article  Google Scholar 

  59. Schwall M, Gerdes JC (2001) Multi-modal diagnostics for vehicle fault detection. In: Proceedings of the IMECE2001, 2001 ASME international mechanical engineering congress and exposition, New York

  60. Schwall ML, Gerdes JC (2002) A probabilistic approach to residual processing for vehicle fault detection. In: Proceedings of the American controls conference, Oakland, CA, pp 2552–2557

  61. Schwall ML, Gerdes JC, Baker B et al (2003) A probabilistic vehicle diagnostic system using multiple models. In: Proceedings of the fifteenth conference on innovative applications of artificial intelligence, Acapulco, Mexico, pp 123–128

  62. Sennechael D, Maclean I (2001) Car-sequencing challenge at a nissan plant calls for complex scheduling. APICS–Performance Advantage, pp 39–40

  63. Smirnov AV, Sheremetov LB, Chilov N et al (2004) Soft-computing technologies for configuration of cooperative supply chain. Appl Soft Comput 4:87–107

    Article  Google Scholar 

  64. Struss P, Price C, (2003) Model-based systems in the automotive industry. AI Mag 24:17–34

    Google Scholar 

  65. Syed F, Ying H, Kuang M et al (2006) Rule-based fuzzy gain scheduling PI controller to improve engine speed and power behavior in a power-split hybrid electric vehicle. In: Proceedings of the North American fuzzy information processing conference (NAFIPS'06), Montreal, Canada, 2006, pp 302–308

  66. Takahashi H (1988) Automatic speed control device using self-tuning fuzzy logic. In: Proceedings of the IEEE workshop on automotive applications of electronics, Dearborne, MI, pp 65–71

  67. Takahashi H (1995) Fuzzy applications for automobiles. In: Hirota K, Sugeno M (eds) Industrial applications of fuzzy technology in the world. World Scientific, Singapore

    Google Scholar 

  68. Tascillo A, DiMeo D, Macneille P et al (2002) Predicting a vehicle or pedestrian's next move with neural networks. In: Proceedings of the international joint conference of neural networks, Honolulu, HI, pp 2310–2314

  69. Uthurusamy R (2003) Trends and challenges in the industrial applications of KDD. In: Proceedings of the Pacific Asia conference on knowledge discovery and data mining, Seoul, Korea. Springer Lect Notes Comput Sci 2637:14

    Article  Google Scholar 

  70. Vachtsevanos G, Wang P (2001) Fault prognosis using dynamic wavelet neural networks. In: Proceedings of the AUTOTESTCON 2001, IEEE systems readiness technology conference, Philadelphia, PA, pp 857–870

  71. Von Altrock C (1997) Fuzzy logic in automotive engineering. Circuit Cellar Ink, issue 88

  72. Weil H-G, Probst FG (1994) Fuzzy expert system for automatic transmission control. In: Marks RJ II (ed) IEEE technology update series: fuzzy logic technology and applications. IEEE, Piscataway, NJ, pp 88–93

    Google Scholar 

  73. Wolford D, Kwiecien S (2002) Driving knowledge management at Ford. In: Handbook on knowledge management, vol 2, Chap 55, Springer, Berlin Heidelberg New York

    Google Scholar 

  74. Wu X, Philips Y, Paitetsky-Shapiro et al (2003) Data mining: how research meets practical development? Know Inf Syst 5(2):248–261

    Article  Google Scholar 

  75. Zarour N, Boufaida M, Seinturier L et al (2005) Supporting virtual enterprise systems using agent coordination. Knowl Inf Syst 8(3):330–349

    Article  Google Scholar 

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Authors and Affiliations

  1. Ford Motor Company, 2101 Village Road, Dearborn, MI, USA

    Oleg Gusikhin, Nestor Rychtyckyj & Dimitar Filev

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  1. Oleg Gusikhin
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  2. Nestor Rychtyckyj
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  3. Dimitar Filev
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Correspondence to Oleg Gusikhin.

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Oleg Gusikhin received the Ph.D. degree from St. Petersburg Institute of Informatics and Automation of the Russian Academy of Sciences and the M.B.A. degree from the University of Michigan, Ann Arbor, MI. Since 1993, he has been with the Ford Motor Company, where he is a Technical Leader at the Ford Manufacturing and Vehicle Design Research Laboratory, and is engaged in different functional areas including information technology, advanced electronics manufacturing, and research and advanced engineering. He has also been involved in the design and implementation of intelligent control applications for manufacturing and vehicle systems. He is the recipient of the 2004 Henry Ford Technology Award. He holds two U.S. patents and has published over 30 articles in refereed journals and conference proceedings. He is an Associate Editor of the International Journal of Flexible Manufacturing Systems. He is also a Certified Fellow of the American Production and Inventory Control Society and a member of IEEE and SME.

Nestor Rychtyckyj received the Ph.D. degree in computer science from Wayne State University, Detroit, MI. He is a technical expert in Artificial Intelligence at Ford Motor Company, Dearborn, MI, in Advanced and Manufacturing Engineering Systems. His current research interests include the application of knowledge-based systems for vehicle assembly process planning and scheduling. Currently, his responsibilities include the development of automotive ontologies, intelligent manufacturing systems, controlled languages, machine translation and corporate terminology management. He has published more than 30 papers in referred journals and conference proceedings. He is a member of AAAI, ACM and the IEEE Computer Society.

Dimitar P. Filev received the Ph.D. degree in electrical engineering from the Czech Technical University, Prague, in 1979. He is a Senior Technical Leader, Intelligent Control and Information Systems with Ford Research and Advanced Engineering specializing in industrial intelligent systems and technologies for control, diagnostics and decision making. He is conducting research in systems theory and applications, modeling of complex systems, intelligent modeling and control, and has published 3 books and over 160 articles in refereed journals and conference proceedings. He holds 14 granted U.S. patents and numerous foreign patents in the area of industrial intelligent systems He is the recipient of the 1995 Award for Excellence of MCB University Press. He was awarded the Henry Ford Technology Award four times for development and implementation of advanced intelligent control technologies. He is an Associate Editor of International Journal of General Systems and International Journal of Approximate Reasoning. He is a member of the Board of Governors of the IEEE Systems, Man and Cybernetics Society and President of the North American Fuzzy Information Processing Society (NAFIPS).

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Gusikhin, O., Rychtyckyj, N. & Filev, D. Intelligent systems in the automotive industry: applications and trends. Knowl Inf Syst 12, 147–168 (2007). https://doi.org/10.1007/s10115-006-0063-1

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