| Abstract: | To ensure secure decision-making for vehicles on highways, the paper proposes a decision-making model that combines deep reinforcement learning (DRL) with a risk correction method. Firstly, the driving data from the target vehicle and its surrounding vehicles is collected, which is essential for the decision-making model. And the attention mechanism is introduced to improve vehicle''s awareness of potentially dangerous vehicles in its surroundings, particularly in complex high-speed scenarios. Then the reward function of the reinforcement learning is designed considering factors such as travel efficiency and obstacle avoidance. In addition, to address the lack of security assurance in the decision-making process of reinforcement learning, the paper proposes a risk correction module, which performs risk assessments and corrections to avoid the execution of potentially dangerous actions. Finally, the proposed decision-making model is trained and validated on the Highway-env simulation platform. The evaluation results show that the proposed approach exhibits better driving safety and robustness. In terms of driving efficiency, it also surpasses the rule-based method, imitation learning and the pure DRL algorithm. |
