Abstract Computational intelligence is traditionally understood as encompassing artificial neural, fuzzy and evolutionary methods and associated computational techniques. Nowadays there is no sharp boundary between CI and other learning methods. Different CI methodologies often get combined with each other and with non-CI methods to achieve superior results in various applications. In this presentation I will discuss CI methodological issues and illustrate them with several applications from the areas of vehicle manufacturing, vehicle system monitoring and control, as well as active safety. These will be representative of CI applications in the industry and beyond. I will also discuss some lessons learned about successful and yet-to-be-successful industrial applications of CI.

Dr. Danil Prokhorov began his technical career in St. Petersburg, Russia, in 1992. He was a research engineer in St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences. He became involved in automotive research in 1995 when he was a Summer intern at Ford Scientific Research Lab in Dearborn, MI. In 1997 he became a Ford Research staff member involved in application-driven research on neural networks and other machine learning methods. While at Ford, he took active part in several production-bound projects including neural network based engine misfire detection. Since 2005 he is with Toyota Technical Center, Ann Arbor, MI, overseeing important mid- and long-term research projects in computational intelligence. He has more than 100 papers in various journals and conference proceedings, as well as several inventions, to his credit. His personal home page is http://home.comcast.net/~dvp/