Optimal Safety Planning and Driving Decision-Making for Multiple Autonomous Vehicles: A Learning Based Approach

Abstract

In the early diffusion stage of autonomous vehicle systems, the controlling of vehicles through exacting decision-making to reduce the number of collisions is a major problem. This paper offers a DRL-based safety planning decision-making scheme in an emergency that leads to both the first and multiple collisions. Firstly, the lane-changing process and braking method are thoroughly analyzed, taking into account the critical aspects of developing an autonomous driving safety scheme. Secondly, we propose a DRL strategy that specifies the optimum driving techniques. We use a multiple-goal reward system to balance the accomplishment rewards from cooperative and competitive approaches, accident severity, and passenger comfort. Thirdly, the deep deterministic policy gradient (DDPG), a basic actor-critic (AC) technique, is used to mitigate the numerous collision problems. This approach can improve the efficacy of the optimal strategy while remaining stable for ongoing control mechanisms. In an emergency, the agent car can adapt optimum driving behaviors to enhance driving safety when adequately trained strategies. Extensive simulations show our concept’s effectiveness and worth in learning efficiency, decision accuracy, and safety.