AI 中文总结
本文提出基于RSU的V2I2V协作自动驾驶系统，结合全局与局部DT及HRL框架，经仿真与PoC试验验证，可提升智能交叉口的安全性与交通效率。
AI 中文摘要
交叉口仍是城市道路网络中最危险的地点之一，异构交通参与者与有限的视野频繁引发严重交通冲突。本文提出一种车-路-车（V2I2V）协作系统，通过部署在路侧单元（RSU）上的数字孪生（DTs）消除盲区，并在智能交叉口集中协调网联与自动驾驶车辆（CAVs），以提升道路安全性与交通效率。该系统集成基于云端的全局DT用于宏观引导，以及基于RSU的局部DT用于实时操作。在该架构内，分层强化学习（HRL）框架结合离线预训练与在线微调，实现鲁棒的协作控制。实验结果显示，所提系统在仿真实验与现实世界的概念验证（PoC）试验中，安全性与效率均有显著提升：在仿真中，系统在实际通信与交通约束下保障了高安全性、效率与平顺性；在PoC试验中，以RSU为中心的控制回路实现约42毫秒的决策延迟，为行人维持8.5米的安全停车距离，同时缩短了停车时长与总通行时间。这些结果表明，所提系统在智能交叉口具备鲁棒且可扩展的性能。
英文摘要
Intersections remain one of the most hazardous locations in urban road networks, where heterogeneous traffic participants and limited visibility frequently lead to severe traffic conflicts. In this paper, a vehicle-to-infrastructure-to-vehicle (V2I2V) cooperative system is proposed for improving road safety and traffic efficiency by using digital twins (DTs) deployed on roadside units (RSUs) to eliminate blind spots and centrally coordinate connected and automated vehicles (CAVs) in smart intersections. The proposed system integrates cloud-based global DTs for macroscopic guidance and RSU-based local DTs for real-time operations. Within this architecture, a hierarchical reinforcement learning (HRL) framework combines offline pre-training with online fine-tuning to achieve robust cooperative control. Experimental results show that the proposed system achieves substantial improvements in safety and efficiency in simulation experiments and real-world proof-of-concept (PoC) trials. In simulations, our system ensures high safety, efficiency, and smoothness under realistic communications and traffic constraints. In PoC trials, the RSU-centric control loop achieves a decision-making latency of approximately 42 ms and maintains a safe stopping distance of 8.5 m for pedestrians, while also shortening stop duration and overall traversal time. These results indicate that the proposed system provides robust and scalable performance at smart intersections.
