讲座报告主题：Future Mobility：Cloud-Enabled Automotive Decision-Making Systems
主讲简介：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. Dr. Li was a recipient of the National Scholarship from China.
主讲内容：Interest in employing cloud computing for automotive applications is growing to support computation and data intensive tasks. The cloud can provide access to “big data" as well as real-time crowd-sourced information. Smart utilization of on-demand cloud resources can increase situation awareness and provide additional functionalities. 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. In this research, we developed an optimal state estimator for systems driven by jump-diffusion process. The developed estimator, together with an input observer, was used to estimate road profile and detect road anomalies such as potholes and speed bumps. I will also present an evolving clustering algorithm that is used to process the anomaly reports. Recent work on model-based safe reinforcement learning will also be discussed.