@article{ref1,
title="Reinforcement learning of dynamic collaborative driving Part II: lateral adaptive control",
journal="International journal of vehicle information and communication systems",
year="2008",
author="Ng, Luke and Clark, Christopher M. and Huissoon, Jan Paul",
volume="1",
number="3/4",
pages="229-248",
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.<p />",
language="",
issn="1471-0242",
doi="10.1504/IJVICS.2008.022356",
url="http://dx.doi.org/10.1504/IJVICS.2008.022356"
}