TY  - JOUR
PY  - 2008//
TI  - Reinforcement learning of dynamic collaborative driving Part I: longitudinal adaptive control
JO  - International journal of vehicle information and communication systems
A1  - Ng, Luke
A1  - Clark, Christopher M.
A1  - Huissoon, Jan Paul
SP  - 208
EP  - 228
VL  - 1
IS  - 3/4
N2  - 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 /> 
LA  - 
SN  - 1471-0242
UR  - http://dx.doi.org/10.1504/IJVICS.2008.022355
ID  - ref1
ER  -