Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.

翻译：交通信号控制对日常生活具有关键安全性。在美国，约四分之一的道路事故因信号配时问题发生在交叉路口，这迫切要求发展以安全为导向的交叉口控制技术。然而，现有基于强化学习技术的自适应交通信号控制研究主要聚焦于最小化交通延误，却忽视了不安全条件的潜在暴露风险。我们首次将道路安全标准作为约束条件引入现有强化学习方法，旨在实现零碰撞的交叉路口运营。我们提出了一种安全增强的残差强化学习方法（SafeLight），并采用多目标损失函数、奖励塑形等多种优化技术以实现更好的知识融合。基于合成数据集和真实世界基准数据集的大量实验表明，我们的方法能在提升交通机动性的同时显著减少碰撞事件。