论文标题
提防威胁安全的演员：主动缓解安全危害
Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards
论文作者
论文摘要
尽管成功展示了自动驾驶汽车（AV），例如自动驾驶汽车，但确保AV安全仍然是一项艰巨的任务。尽管某些演员比其他演员对AV的驾驶决策的影响更大，但目前的方法对道路上的每个演员都同样关注。演员对AV决定的影响可以从减少AV的安全导航选择数量的能力来表征。在这项工作中，我们使用反事实推理提出了一个安全威胁指标（STI），以估算每个演员对AV安全性的影响。我们使用此指标来（i）表征现有的现实世界数据集，以识别罕见的危险场景以及在这种情况下现有控制器的性能不佳； （ii）设计基于RL的安全缓解控制器，以主动减轻这些演员对AV的安全危害。我们的方法在极少数危险情况下，最先进的AV代理的事故率降低了70％以上。
Despite the successful demonstration of autonomous vehicles (AVs), such as self-driving cars, ensuring AV safety remains a challenging task. Although some actors influence an AV's driving decisions more than others, current approaches pay equal attention to each actor on the road. An actor's influence on the AV's decision can be characterized in terms of its ability to decrease the number of safe navigational choices for the AV. In this work, we propose a safety threat indicator (STI) using counterfactual reasoning to estimate the importance of each actor on the road with respect to its influence on the AV's safety. We use this indicator to (i) characterize the existing real-world datasets to identify rare hazardous scenarios as well as the poor performance of existing controllers in such scenarios; and (ii) design an RL based safety mitigation controller to proactively mitigate the safety hazards those actors pose to the AV. Our approach reduces the accident rate for the state-of-the-art AV agent(s) in rare hazardous scenarios by more than 70%.
