Reinforcement Learning of Adaptive Longitudinal 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. Each vehicle involved is a composite nonlinear system powered by an internal combustion engine, equipped with automatic transmission, rolling on rubber tires 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 which uses Monte Carlo Reinforcement Learning is introduced. 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.