@article{ref1,
title="Reinforcement learning of dynamic collaborative driving Part I: longitudinal 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="208-228",
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.<p />",
language="",
issn="1471-0242",
doi="10.1504/IJVICS.2008.022355",
url="http://dx.doi.org/10.1504/IJVICS.2008.022355"
}