Reinforcement Learning of Dynamic Collaborative Driving

Abstract

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.