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：交通信号控制对我们日常生活的安全至关重要。大约四分之一的美国公路事故发生在十字路口,原因是信号时间不对,敦促发展面向安全的交叉控制。然而,目前关于利用强化学习技术进行适应性交通信号控制的研究主要侧重于尽量减少交通延误,但忽视了可能暴露于不安全条件的可能性。我们首次将道路安全标准作为执法措施纳入其中,以确保现有强化学习方法的安全,目标是操作零碰撞的交叉点。我们提出了加强安全性残余强化学习方法(SafeLight),并采用多种优化技术,例如多目标损失功能和奖励形成更好的知识整合。正在使用合成和现实世界基准数据集进行广泛的实验。结果显示,我们的方法可以大大减少碰撞,同时增加交通流动性。</s>