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.

翻译：端到端自动驾驶系统凭借其在环境感知和泛化驾驶决策方面的强大能力，正日益受到学术界和工业界的广泛关注。然而，一旦部署在公共道路上，自动驾驶系统不可避免地会暴露于各种可能危及安全并降低系统性能的驾驶风险之中。这对其韧性提出了强烈需求，特别是持续监控驾驶风险并自适应响应潜在安全违规的能力，这对于在复杂驾驶场景中保持稳健驾驶行为至关重要。为弥补这一空白，我们提出了一个运行时韧性导向框架——Argus，以缓解驾驶风险，从而预防潜在的安全违规并提升自动驾驶系统的驾驶性能。Argus持续监控自动驾驶系统生成的轨迹以识别潜在风险，一旦自车被判定为处于不安全状态，便通过风险缓解器无缝接管控制。我们将Argus与三种先进的端到端自动驾驶系统（即TCP、UniAD和VAD）进行了集成。评估结果表明，Argus能有效且高效地增强自动驾驶系统的韧性，平均将系统的驾驶评分提升高达150.30%，并预防多达64.38%的违规行为，同时仅引入极小的额外时间开销。