Reinforcement learning of dynamic collaborative driving Part I: longitudinal adaptive control
by Luke Ng, Christopher M. Clark, Jan Paul Huissoon
International Journal of Vehicle Information and Communication Systems (IJVICS), Vol. 1, No. 3/4, 2008

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. Each vehicle involved is a composite non-linear system powered by an internal combustion engine, equipped with automatic transmission, rolling on rubber tyres with a hydraulic braking system. This paper focuses on the problem of longitudinal motion control. A longitudinal vehicle model is introduced which serves as the control system design platform. A longitudinal adaptive control system that uses Monte Carlo Reinforcement Learning (RL) is introduced. The results of the RL phase and the performance of the adaptive control system for a single automobile, as well as the performance in a multi-vehicle platoon, are presented.

Online publication date: Fri, 02-Jan-2009

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