Title
Reinforcement Learning of Adaptive Longitudinal Vehicle Control for Dynamic Collaborative Driving
Recommended Citation
Published in Proceedings of the 2008 IEEE Intelligent Vehicles Symposium: Eindhoven, The Netherlands, June 4, 2008, pages 907-912.
Copyright © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. The definitive version is available at http://dx.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
Download
URL: http://digitalcommons.calpoly.edu/csse_fac/56