Intersection decision making for autonomous vehicles based on improved PPO algorithm

Abstract

Abstract The deployment of autonomous vehicles (AVs) in complex urban environments faces numerous challenges, especially at intersections where they coexist with human‐driven vehicles (HVs), resulting in increased safety risks. In response, this study proposes an improved control strategy based on the Proximal Policy Optimization (PPO) algorithm, specifically designed for hybrid intersections, known as MSA‐PPO. First, the Self‐Attention Mechanism (SAM) is introduced into the algorithmic framework to quickly identify the surrounding vehicles with a greater impact on the ego vehicle from different perspectives, accelerating data processing and improving decision quality. Second, an invalid action masking mechanism is adopted to reduce the action space, ensuring actions are only selected from feasible sets, thereby enhancing decision efficiency. Finally, comparative and ablation experiments in hybrid intersection simulation environments of varying complexity are conducted to validate the algorithm's effectiveness. The results show that the improved algorithm converges faster, achieves higher decision accuracy, and demonstrates the highest speed levels during driving compared to other baseline algorithms.

Keywords

• artificial intelligence
• automated driving and intelligent vehicles
• autonomous driving
• Markov processes