arXiv:2608. 11451v1 Announce Type: cross Abstract: Modern end-to-end driving agents can achieve high average performance yet still violate basic traffic rules that a human driver would never miss.
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arXiv:2610.09294v1 Announce Type: cross Abstract: Rapid progress in AI agents has brought growing attention to agent safety, with extensive evaluation focused on digital environments. As agents move...
The paper introduces CoPhy, a Cognitive‑Physical reinforcement learning framework for autonomous driving that addresses the limitations of end‑to‑end models by combining a cognitive foundation and a foresighted physical environment. CoPhy distills vision‑language model knowledge into a BEV encoder, then removes the VLM to keep cognitive capability at zero inference cost while allowing optional human language commands. An auto‑regressive BEV world model predicts future semantic maps conditioned on candidate actions, providing an interpretable sandbox for safety metrics, and the driving policy is optimized with a dual‑reward mechanism that enforces safety constraints and intent compliance. Extensive experiments show CoPhy achieves state‑of‑the‑art results on NAVSIM v1 and v2 and enables safer, intent‑controlled driving.
CoSafe is a modular, ADS-agnostic framework that extends runtime safety assurance from ego-centric takeover to scene-level cooperative intervention in mixed-traffic autonomous driving. It uses vehicle-to-everything communication to allow roadside units to monitor hazards and jointly mitigate risks involving multiple vehicles. Evaluation on the InterDrive benchmark shows that CoSafe improves driving scores by 70.03%, reduces safety violations by 87.80%, and accurately identifies unsafe autonomous vehicles with an F3 score of 0.930 while meeting real-time constraints.
The paper introduces NegWAM, a safety post‑training framework for unified world‑action models (WAMs) used in end‑to‑end autonomous driving. NegWAM generates negative trajectories that are close to expert paths but contain safety risks, using them as repulsive anchors to steer predictions away from dangerous behavior while encoding scene‑trajectory compatibility via rider tokens. The method achieves a 4.1% relative improvement on the NAVSIM v2 navhard benchmark and 93.3 EPDMS on navtest, demonstrating strong safety performance without sacrificing routine driving, and shows cross‑benchmark generalization in zero‑shot closed‑loop tests on HUGSIM.
OPTED is a method for on‑policy fine‑tuning of end‑to‑end driving models that separates reinforcement learning from the policy update. A privileged teacher trained with RL on vectorized inputs (HD‑maps and bounding boxes) supervises the pre‑trained student during closed‑loop post‑training. Applied to the camera‑based models TransFuser and VaVAM in AlpaSim, OPTED boosts driving scores by 1.6× and 9.5×, respectively, while requiring roughly three orders of magnitude fewer simulator interactions than direct RL post‑training.
arXiv:2604. 15579v2 Announce Type: replace-cross Abstract: There is increasing interest in integrating AI agents that invoke tools into domain-specific commercial software, where unintended tool calls can cause serious security and safety incidents.