Reinforcement Learning of Adaptive Longitudinal Vehicle Control for Dynamic Collaborative Driving

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. This paper focuses on the problem of longitudinal motion control. A detailed nonlinear longitudinal vehicle model which serves as the control system design platform is used to develop a longitudinal adaptive control system based on Monte Carlo Reinforcement Learning. The results of the reinforcement learning phase and the performance of the adaptive control system for a single automobile as well as the performance in a multi-vehicle platoon is presented.