SBF-DRL: A Multi-Vehicle Safety Enhancement Framework Based on Deep Reinforcement Learning with Integrated Safety Barrier Function

Although deep reinforcement learning has achieved great success in the field of autonomous driving, it still faces technical obstacles, such as balancing safety and efficiency in complex driving environments. This paper proposes a deep reinforcement learning multi-vehicle safety enhancement framework that integrates a safety barrier function (SBF-DRL). SBF-DRL first provides independent monitoring assurance for each autonomous vehicle through redundant functions and maintains safety in local vehicles to ensure the safety of the entire multi-autonomous vehicle driving system. Secondly, combining the safety barrier function constraints and the deep reinforcement learning algorithm, a meta-control policy using Markov Decision Process modeling is proposed to provide a safe logic switching assurance mechanism. The experimental results show that SBF-DRL’s collision rate is controlled below 3% in various driving scenarios, which is far lower than other baseline algorithms, and achieves a more effective trade-off between safety and efficiency.