arXiv cs.AI· Dingji Wang, You Lu, Haowen Jiang, Yanggang Chen, Yuyang Bi, Bihuan Chen, Shenglin Bao, Xin Peng·· 11 小时前AI 评分34
CoSafe：面向混合交通自动驾驶的协同运行时安全保证框架
CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving
AI 导读
CoSafe 是一个模块化、与 ADS 无关的协同运行时安全保证框架，通过 V2X 通信让路侧单元监控场景级风险并联合干预多车冲突。与 7 个 SOTA ADS 集成后，在 InterDrive 基准上平均提升驾驶得分 70.03%、减少安全违规 87.80%，识别不安全 AV 的 F3 分数达 0.930，并满足实时约束。该工作已被 ICSE 2027 接收。
正文 · 原文
Abstract:With the rapid development of autonomous driving systems (ADSes), real-world roads are evolving into mixed-traffic environments where autonomous vehicles (AVs) and other traffic participants (e.g., human-driven vehicles (HDVs), cyclists, and pedestrians) coexist and interact with each other. However, current ADSes still incur various safety violations in mixed traffic scenarios, raising a strong demand for runtime safety assurance frameworks that can continuously monitor hazards and prevent potential violations. Existing ego-centric assurance approaches are insufficient for mixed traffic, where violations often arise from locally reasonable yet jointly incompatible decisions among multiple vehicles and cannot be reliably resolved through independent takeover.
To bridge this gap, we propose CoSafe, a modular and ADS-agnostic cooperative runtime safety assurance framework for mixed-traffic autonomous driving to extend runtime safety assurance from ego-centric takeover to scene-level cooperative intervention. CoSafe leverages vehicle-to-everything (V2X) communication to enable roadside units (RSUs) to monitor scene-level hazards and jointly mitigate hazards involving multiple vehicles. We integrate CoSafe with seven state-of-the-art ADSes, and our evaluation on the InterDrive benchmark demonstrates that CoSafe improves the driving score of AVs by 70.03% on average, reduces safety violations by 87.80%, and accurately identifies unsafe AVs with an F3 score of 0.930, while satisfying real-time constraints.
| Comments: | Accepted by the 49th IEEE/ACM International Conference on Software Engineering (ICSE 2027). 13 pages |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2611.06204 [cs.SE] |
| (or arXiv:2611.06204v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06204 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dingji Wang [view email]
[v1]
Wed, 4 Nov 2026 09:21:22 UTC (1,049 KB)
来源：arXiv cs.AI · arxiv.org