Reinforcement learning of dynamic collaborative driving Part II: lateral adaptive control Online publication date: Fri, 02-Jan-2009
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: In dynamic collaborative driving, multiple vehicles coordinate their motion to optimise road usage using shared information. The basic prerequisites for a vehicle participating in dynamic collaborative driving are longitudinal and lateral control. This paper focuses on the lateral vehicle control on which higher-level manoeuvres such as entering or exiting a formation are based. Each vehicle involved is a composite nonlinear system powered by an internal combustion engine, equipped with automatic transmission, rolling on rubber tyres with hydraulic braking systems and steering system. A vehicle model is introduced which serves as the control system design platform. A lateral adaptive preview control system which uses Monte Carlo Reinforcement Learning (RL) is introduced. The results of the RL phase and the performance of the adaptive preview control system for a single automobile as well as the performance in a multi-vehicle platoon are presented.
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