关键词: 智能汽车, 智能刹车, Q学习, 线性规划

Abstract: Addressing the safety issues of intelligent vehicles operating in mixed traffic flows, an improved Q-learning-based algorithm for safe braking of intelligent vehicles in multiple scenarios is proposed. Firstly, a three-vehicle model is established based on road conditions and vehicle parameters, with scenarios of braking, following, and lane changing being simulated separately. Secondly, linear programming is applied to the training data to ensure the possibility of safe braking for intelligent vehicles. Finally, a reward function is designed to guide the agent in controlling the middle vehicle to remain between the front and rear vehicles while ensuring safe braking. Additionally, the algorithm incorporates an interval-block method to handle continuous state space issues. A comparative test between the proposed algorithm and the traditional Q-learning algorithm in three common driving scenarios reveals that the proposed algorithm outperforms the traditional Q-learning algorithm in terms of safety and training efficiency, effectively controlling the middle vehicle to remain between the front and rear vehicles while ensuring safety in braking, following, and lane changing scenarios.

Key words: Keywords: intelligent vehicle, intelligent braking, Q-learning, linear programming