Abstract
Lane changing in dense traffic is a significant challenge for Connected and Autonomous Vehicles (CAVs). Existing lane change controllers primarily either ensure safety or collaboratively improve traffic efficiency, but do not consider these conflicting objectives together. To address this, we propose the Multi-Agent Safety Shield (MASS), designed using Control Barrier Functions (CBFs) to enable safe and collaborative lane changes. The MASS enables collaboration by capturing multi-agent interactions among CAVs through interaction topologies constructed as a graph using a simple algorithm. Further, a state-of-the-art Multi-Agent Reinforcement Learning (MARL) lane change controller is extended by integrating MASS to ensure safety and defining a customised reward function to prioritise efficiency improvements. As a result, we propose a lane change controller, known as MARL-MASS, and evaluate it in a congested on-ramp merging simulation. The results demonstrate that MASS enables collaborative lane changes with safety guarantees by strictly respecting the safety constraints. Moreover, the proposed custom reward function improves the stability of MARL policies trained with a safety shield. Overall, by encouraging the exploration of a collaborative lane change policy while respecting safety constraints, MARL-MASS effectively balances the trade-off between ensuring safety and improving traffic efficiency in congested traffic. The code for MARL-MASS is available with an open-source licence at this https URL
Abstract (translated)
车道变换在密集交通中是连接和自主车辆（CAVs）面临的重要挑战。现有的车道变更控制器主要专注于确保安全或协作提高交通效率，但没有同时考虑这些相互冲突的目标。为了解决这个问题，我们提出了多智能体安全盾（Multi-Agent Safety Shield, MASS），该系统采用控制屏障函数（Control Barrier Functions, CBFs）设计，旨在实现安全且协同的车道变换。 MASS通过构建一个使用简单算法生成的图来捕捉CAVs之间的多代理互动，从而促进协作。此外，通过整合MASS并定义定制化的奖励函数以优先考虑效率改进，将最先进的多代理强化学习（Multi-Agent Reinforcement Learning, MARL）车道变更控制器进行了扩展，确保了安全性。 因此，我们提出了一种称为MARL-MASS的车道变换控制器，并在拥堵上匝道汇流仿真中对其进行了评估。结果表明，MASS通过严格遵守安全限制来促进协同车道变换并提供安全保障。此外，所提出的定制奖励函数提高了使用安全盾训练的MARL策略的稳定性。 总的来说，通过鼓励探索符合安全约束条件下的协作车道变更政策，MARL-MASS有效地平衡了在拥堵交通中确保安全和提高交通效率之间的权衡关系。MARL-MASS的相关代码可以在这个开源许可链接下获得：[https URL]（请将URL更改为实际提供的链接地址）。
URL
https://arxiv.org/abs/2602.10007
