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CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving

1 reports1 reporting sourcesUpdated 5 days ago

Overview

Event synthesis

This paper introduces CoSafe, a modular and ADS-agnostic framework designed to ensure runtime safety in mixed-traffic environments by leveraging vehicle-to-everything (V2X) communication. The system enables roadside units to monitor scene-level hazards and coordinate interventions among multiple vehicles, addressing limitations of ego-centric approaches. Evaluated on the InterDrive benchmark with seven state-of-the-art autonomous driving systems, CoSafe achieves an average 70.03% improvement in driving scores, reduces safety violations by 87.80%, and identifies unsafe vehicles with an F3 score of 0.930 while meeting real-time constraints.

Generated from attributed reports · Updated 3 days ago

Event evidence and corrections

0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.

Reported quantity · F3 score: 0.93 other · Basis not reported
Supporting report

“rate 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.”

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11/5
  1. arXiv Robotics — research abstractsSignal
    CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving

    This paper introduces CoSafe, a modular and ADS-agnostic framework designed to ensure runtime safety in mixed-traffic environments by leveraging vehicle-to-everything (V2X) communication. The system enables roadside units to monitor scene-level hazards and coordinate interventions among multiple vehicles, addressing limitations of ego-centric approaches. Evaluated on the InterDrive benchmark with seven state-of-the-art autonomous driving systems, CoSafe achieves an average 70.03% improvement in driving scores, reduces safety violations by 87.80%, and identifies unsafe vehicles with an F3 score of 0.930 while meeting real-time constraints.

Event coverage history

Current attention 3·Peak within the comparable range 3(2026-11-07 01:00 UTC)·Change within the comparable range over 24 hours –

01232026-11-0701:002026-11-0702:002026-11-0703:002026-11-0704:00

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