一句话：提出运行时框架 Argus，持续监控端到端自动驾驶轨迹并缓解危险，提升驾驶分数并减少违规。
端到端自动驾驶系统（ADS）在感知与决策上表现突出，但部署到公开道路后会面临多种驾驶危险，可能损害安全性并降低性能，因此需要持续监控危险并自适应响应安全违规的韧性能力。为此，本文提出面向韧性的运行时框架 Argus，持续监控 ADS 生成的轨迹以发现潜在危险；当判定 EGO 车辆不安全时，通过危险缓解器无缝接管控制，从而避免潜在安全违规并改善驾驶表现。作者将 Argus 与 TCP、UniAD、VAD 三个先进端到端 ADS 集成。评估表明，Argus 能有效且高效地增强 ADS 韧性：平均提升驾驶分数 150.30%，阻止 64.38% 的违规，且额外时间开销很小。
End-to-end autonomous driving systems (ADSs), with their strong capabilities in environmental perception and generalizable driving decisions, are attracting growing attention from both academia and industry. However, once deployed on public roads, ADSs are inevitably exposed to diverse driving hazards that may compromise safety and degrade system performance. This raises a strong demand for resilience of ADSs, particularly the capability to continuously monitor driving hazards and adaptively respond to potential safety violations, which is crucial for maintaining robust driving behaviors in complex driving scenarios.To bridge this gap, we propose a resilience-oriented runtime framework, named Argus, to mitigate the driving hazards, thus preventing potential safety violations and improving the driving performance of an ADS. Argus continuously monitors the trajectories generated by the ADS for potential hazards and, whenever the EGO vehicle is deemed unsafe, seamlessly takes control via a hazard mitigator. We integrate Argus with three state-of-the-art end-to-end ADSs, i.e., TCP, UniAD and VAD. Our evaluation has demonstrated that Argus effectively and efficiently enhances the resilience of ADSs, improving the driving score of ADSs by 150.30% on average, and preventing 64.38% of the violations, with little additional time overhead.
DOI 原文 ·
@inproceedings{paperbot9363,
title = {Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs},
author = {Dingji Wang and You Lu and Bihuan Chen and Shuo Hao and Haowen Jiang and Yifan Tian and Xin Peng},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00010}
}