Decision-Making Method Combining Path-Constraint-Based Action Compensation for Autonomous Vehicles

Abstract

To improve the performance and reliability of the decision-making process for autonomous vehicles, a decision-making method using deep reinforcement learning (DRL) and combining path-constraint-based action compensation is proposed in this article. In the decision-making method, an action compensation module based on path constraints is developed, which enhances the reliability of the decision of vehicle and ensures that the final output actions meet safety and comfort requirements. In addition, the traditional reinforcement learning task is divided into three subtasks, and the switching between the subtasks is realized by the scene recognition module. It reduces the complexity of the task and improves the training efficiency and sample utilization. Moreover, rewards and penalties are designed within path constraints, incorporating the distance from the lane center and the heading deviation angle. This can provide efficient and timely feedback for the vehicle during environmental interactions, accelerating the training process and improving decision-making performance. Finally, experiments are conducted using the CARLA simulation platform. The results show that the proposed method not only enables reliable decision-making but also improves the performance of vehicle, ensuring smooth control of the vehicle through the driving task, which verifies the effectiveness of the proposed method.