A Rear Anti-Collision Decision-Making Methodology Based on Deep Reinforcement Learning for Autonomous Commercial Vehicles

Abstract

Driving decision-making determines the safety and rationality of autonomous commercial vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision decision-making methodology based on deep reinforcement learning (RAD-DRL) was creatively proposed. Firstly, aiming at the dynamic coupling of rear anti-collision factors for safe driving, a driving decision network based on an actor-critic framework was proposed for sensor data processing. Then, inspired by the idea of multi-objective optimization, a refined reward function is developed. It comprehensively considers the impact of backward target types, safety clearance, and vehicle roll stability on the rear collision. Finally, the RAD-DRL was trained (with different values of random seeds), tested, and verified in a simulation environment where road network and traffic situations were built-in SUMO (Simulation of Urban Mobility). After 30,000 training episodes, effective and reliable rear anti-collision driving decisions were achieved by our proposed RAD-DRL. The simulated results indicated that the RAD-DRL has remarkable superiority in generalization and effectiveness in expressway scenarios with different driving cases. Especially, it maintained good decision performance in the corner case in which the backward vehicle suddenly accelerates.