Date: Wednesday,20th December,10:00-11:30
Site: Zonghe Building 424 综合楼424
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,Iwill first present the Vehicle-to-Cloud-to-Vehicl framework and discuss its opportunities and challenges.The focus of the talk will be the exploitation of automotive vehicles to crowd-source road information.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. Future work on Reinforcement Learning and Connected and Autonomous Vehicles will also be discussed.
Dr.Zhaojian u is an Asistant 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 Ch ina.Dr.liworked as a research eneimeer atGeneral Motors from January 2016 to July 2017.His research interest nelernetednandAutomatedehiehna Control, helientTransportati Systems,and Reinforcement Learning.Dr.Li was a recipient of the Natinal Scholarship from China.