Automated and Connected Vehicles

Research Description

We are developing model-predictive control (MPC) algorithms to integrate planning and motion control for autonomous vehicles. These algorithms are deployed using ROS on the University of Waterloo “Autonomoose”. Vehicle and tire models are developed from experimental tests using our intelligent vehicle measurement system and controllers are evaluated in virtual simulations and hardware-in-loop experiments prior to vehicle testing.

Student Researchers

Yuan Lin
Chi Jin
Anson Maitland (Alumnus)
Mohammad Hassanpour (Alumnus)

Keywords and Themes

• Model-Predictive Controllers
• Autonomous and Connected Vehicles
• Intelligent Vehicle Systems
• Automated Planning and Control  


Related Publications 

• Lin Y, McPhee J, and Azad NL. (2019). Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning. IEEE Intelligent Transportation Systems Conference. arXiv:1905.08314.
• Batra M, McPhee J, and Azad NL. (2018). Real-Time Model Predictive Control of Connected Electric Vehicles. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility. DOI: 10.1080/00423114.2018.1552004.
• Maitland A and McPhee J. (2018). Towards Integrated Planning and Control of Autonomous Vehicles Using Nested MPCs. ASME 2018 Dynamic Systems and Control Conference. DOI: 10.1115/DSCC2018-9224.