报 告 人：Shan Bao（鲍珊）博士
Dr. Bao is an Associate Professor in the Industrial and Manufacturing Systems Engineering Department, University of Michigan-Dearborn. She also has a joint appointment as an Associate Research Scientist at the University of Michigan Transportation Research Institute’s Human Factors Group. She received her Ph.D. in mechanical and industrial engineering from the University of Iowa in 2009. Her research interests focus on human factors issues related to connected and automated vehicle technologies, ADAS system evaluation, and big data analysis. She has served as the PI (with a total funding of 3.4 million dollars) or co-PI (with a total funding of more than 13 million dollars) of 43 research projects. She has published 54 technical publications, including 28 referred journals articles. Shan has served as the chair of the Surface Transportation Technical Group of Human Factors and Ergonomics Society. She is a member of the TRB Vehicle User Characteristics committee and the TRB Human Factors in Road Vehicle Automation subcommittee.
Automated Vehicle (AV) technologies are actively studied in the automotive industry because their potential to reduce crashes, save fuel, ease traffic congestion, and provide better mobility. The timeline for automated driving implementation is fast approaching. With the rapid growth of vehicle sensor and control technologies, the driver-vehicle relationship is changing dramatically. A driver, traditionally viewed as an active operator, will serve as a supervisory controller or just a passenger. This role change has provided new opportunities, as well as challenges to our researchers in the transportation society. For example, the introduction of automation into vehicles has raised considerable interest in knowing whether the driver is sufficiently engaged in the driving task and is aware of the driving situation, such that a successful transfer of control might be possible, or that other assistance should be provided. The speaker will share her vision and her research in driver behavior modeling and state monitoring through her talk. This work lays the foundation for studies on driver state monitoring and detection, as well as future system design.