报告题目:Computational Intelligence in Vehicles and Transportation Systems
报告人：美国密西根大学迪尔本分校 工学院副院长Prof. Yi Lu Murphey
Dr. Yi Lu Murphey received a M.S. degree in computer science from Wayne State University, Detroit, Michigan, in 1983, and a Ph.D degree with a major in Computer Engineering and a minor in Control Engineering from the University of Michigan, Ann Arbor, Michigan, in 1989. She is currently a Full Professor at the ECE (Electrical and Computer Engineering) department and the Associate Dean for Graduate Education and Research at the College of Engineering and Computer Science in the University of Michigan-Dearborn. Prior to her current position, she served as the chair of the Department of Electrical and Computer Engineering for seven years. She has authored over 150 publications in refereed journals and conference proceedings in the areas of areas of machine learning, pattern recognition, computer vision and intelligent systems with applications to intelligent vehicle systems, optimal vehicle power management, data analytics, automated and connected vehicles and robotic vision systems. She has received over $7 million in research grants and contracts over the last twenty years from US National Science Foundation, US Department of Defense, and many industrial companies. Currently her current research in machine learning, computer vision, and data science are funded by Ford Motor Company, ZF-TRW Automotive, University of Michigan Mobility Transformation Center (MTC), Michigan Institute of Data Science (MIDAS) and Toyota Research Institute. Dr. Murphey is a Distinguished Lecturer for the IEEE Society of Vehicular Technologies and a fellow of IEEE.
Abstract: Nearly every facet of our society is undergoing a shift of connecting the individual to the community. The “Internet of Things” movement is giving great power to the individual, by personalizing information that is time and location-aware. In the broad transportation community, building on the momentum and success of prior and current research, two primary areas have been identified as the forefront of ITS (Intelligent Transportation Systems) research, Connected and Automated Vehicles (CAV). Transforming individual vehicles into an integrated cyberphysical system through connectivity and automation can improve vehicle efficiency, convenience and safety to drivers, and reduce greenhouse-gas emissions by an order of magnitude. In this talk, I will present three different research projects in the areas of CAV using computational intelligence, accurate prediction of traffic flow, individual driving speed profiles, and personalized driving routes. I will give an in-depth discussion on machine learning for intelligent traffic flow prediction.