报告人简介：

Dr. Zhaojian Li is an Assistant Professor in the Department of Mechanical Engineering at Michigan State University. He obtained M.S. (2013) and Ph.D. (2015) in Aerospace Engineering (flight dynamics and control) at the University of Michigan, Ann Arbor. As an undergraduate, Dr. Li studied at Nanjing University of Aeronautics and Astronautics, Department of Civil Aviation, in China. Dr. Li worked as an algorithm engineer at General Motors from January 2016 to July 2017. His research interests include Learning-based Control, Nonlinear and Complex Systems, and Robotics and Automated Vehicles. He is the author of more than 20 top journal articles and several patents. He is currently the Associate Editor for Journal of Evolving Systems, American Control Conference and ASME Dynamics and Control Conference.

报告简介：

Data is everywhere. Modern vehicles are equipped with hundreds of sophisticated sensors that offer necessary information for various functionalities. With vehicle connectivity, these vehicles can be exploited as mobile platforms to crowdsource real-time road and traffic information, which can be utilized to enhance the automotive decision-making systems for improved safety, efficient energy, and ride comfort.

In this talk, I will first present the Vehicle-to-Cloud-to-Vehicle framework and discuss its opportunities and challenges. The focus of the talk will be the exploitation of automotive vehicles to crowd-source road information for collaborative comfort and energy harvesting. I will also talk about recent work on online driver identification as well as integrating learning and control for efficient system identification and controls.