Invited Speaker

Dr. Yiqun Li, Associate Professor

Huazhong University of Science & Technology, China

Biography: Yiqun Li is an Associate Professor at the State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST). He received his B.S., M.S., and Ph.D. degrees from the Harbin Institute of Technology in 2011, 2013, and 2017, respectively, and was a visiting Ph.D. student at the University of California, San Diego, from 2014 to 2015. Dr. Li has authored more than 30 papers in leading journals, including Robotics and Computer-Integrated Manufacturing, Journal of Field Robotics, IEEE Robotics and Automation Letters, and Journal of Computational Physics. He also holds over 10 invention patents and co-authored a nationally recognized robotics textbook. His research focuses on robotics, nonlinear geometric control, structure-preserving numerical simulation, and data-driven science and technology. He is particularly interested in developing theoretically grounded yet practically effective approaches for robotic autonomy, with applications ranging from advanced manufacturing to field robotics.

Dr. Jinhao Liang, Research Fellow

National University of Singapore, Singapore

Biograph: Dr. Liang is currently a Research Fellow with Department of Civil and Environmental Engineering, National University of Singapore, Singapore. His research interests have focused on the vehicle dynamics and control, connected and autonomous vehicles, and vehicle-road cooperation systems. Dr. Liang has published more than 50 papers in journals and proceedings of international conferences, and holds more than 10 patents in the field of intelligent vehicle control. He won the Leading Prize for Autonomous Emergency Braking assistance system and Lane-keeping assistance system at the 2017 1st World Intelligent Driving Challenge. He serves as an editorial board member for the Journal of Advanced Transportation and Discover Vehicles, and as a guest editor for the journals Computers and Electrical Engineering and Journal of Engineering. He also serves as a session chair for the 27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024) and 6th International Conference on Advances in Signal Processing and Artificial Intelligence and a Workshop Chair for the 2024 International Conference on Frontiers of Electronic, Electrical, and Computer Science. Additionally, he acts as an Invited Speaker at the 2023 6th International Conference on Mechanical Engineering and Applied Composite Materials.

Speech Title: Key Technology and Application Towards New-generation Intelligent Vehicles

Abstract: Since intelligent vehicles (IVs) have the potential to improve road safety, reduce energy consumption, and mitigate environmental pollution, they have recently become an attractive research field worldwide. IVs are an increasingly important part of intelligent transportation systems, whose intelligence is concentrated on smart and safe driving. The two important components of IVs are the adaptive decision-making and the vehicle motion control. They are not only related but also interactive: the decision-making is the prerequisite, and the vehicle motion control is the purpose. IVs have the characteristics of parameter uncertainty, time delay, and highly nonlinear dynamics, and are a typical complex coupled system. How to realize the precise motion control is key for IVs. This report focuses on the combination of deep reinforcement learning technology and vehicle chassis holistic control theory to promote the adaptive ability of intelligent vehicles in complex driving conditions.