Title

Reinforcement Learning of Adaptive Longitudinal Vehicle Control for Dynamic Collaborative Driving

Author Info

Luke Ng, University of Waterloo
Christopher M. Clark, California Polytechnic State University - San Luis ObispoFollow
Jan P. Huissoon, University of Waterloo

Recommended Citation

Published in Proceedings of the 2008 IEEE Intelligent Vehicles Symposium: Eindhoven, The Netherlands, June 4, 2008, pages 907-912.

The definitive version is available at https://doi.org/10.1109/IVS.2008.4621222.

Abstract

Dynamic collaborative driving involves the motion coordination of multiple vehicles using shared information from vehicles instrumented to perceive their surroundings in order to improve road usage and safety. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal control. Without this capability, higher-level coordination is not possible. This paper focuses on the problem of longitudinal motion control. A detailed nonlinear longitudinal vehicle model which serves as the control system design platform is used to develop a longitudinal adaptive control system based on Monte Carlo reinforcement learning. The results of the reinforcement learning phase and the performance of the adaptive control system for a single automobile as well as the performance in a multi-vehicle platoon is presented.

Disciplines

Computer Sciences

Copyright

2008 IEEE.

Publisher statement

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