Argus为端到端自动驾驶系统提供实时安全防护，自动应对潜在危险。
Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs
- 运行时监控驾驶轨迹，发现危险即触发应急接管
- 平均提升驾驶评分150.30%，最多避免64.38%安全事故
- 可无缝集成主流端到端模型，延迟极低
端到端自动驾驶系统（ADS）在环境感知和泛化决策方面表现出强大能力，受到学术界和产业界广泛关注。然而，一旦部署于公共道路，系统不可避免地面临多样驾驶风险，可能威胁安全并降低性能。因此，亟需提升系统的韧性，特别是持续监测驾驶风险并自适应响应潜在安全违规的能力，以保障复杂场景下的稳健驾驶行为。为此，我们提出一种运行时韧性导向框架Argus，用于缓解驾驶风险，防止潜在安全违规，并提升系统性能。Argus持续监控ADS生成的轨迹，一旦判定本车处于不安全状态，便通过风险缓解模块无缝接管控制。我们将Argus集成至三种先进端到端ADS（TCP、UniAD、VAD），评估表明，Argus能有效且高效地增强系统韧性，在平均驾驶评分上提升达150.30%，最多避免64.38%的安全违规，且额外计算开销极小。
原文摘要 · Abstract (English)
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 runtime resilience-oriented framework, 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 through 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 the ADS by up to 150.30% on average, and preventing up to 64.38% of the violations, with little additional time overhead.
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