论文标题
高级驾驶员辅助系统的正式安全表征，采用方案采样
A Formal Safety Characterization of Advanced Driver Assist Systems in the Car-Following Regime with Scenario-Sampling
论文作者
论文摘要
遵循铅车并避免后端碰撞的能力是人类驾驶员和各种高级驾驶员辅助系统（ADA）的最重要功能之一。现有的安全性绩效为汽车跟随系统的合理性要么依赖于具有偏见的替代指标的简单混凝土场景，要么需要长时间的驾驶距离才能观察和推理。在本文中，我们提出了一个保证的公正和采样有效的基于方案的安全评估框架，该框架灵感来自先前对$εδ$的工作，几乎是安全设置的定量。该提案表征了测试主题在跟随汽车的制度中的完整安全性能。在具有挑战性的案件中，还证明了该方法的性能，包括一些广泛采用的汽车决策模块以及Commaai的商业上可用的OpenPiLot驾驶堆栈。
The capability to follow a lead-vehicle and avoid rear-end collisions is one of the most important functionalities for human drivers and various Advanced Driver Assist Systems (ADAS). Existing safety performance justification of the car-following systems either relies on simple concrete scenarios with biased surrogate metrics or requires a significantly long driving distance for risk observation and inference. In this paper, we propose a guaranteed unbiased and sampling efficient scenario-based safety evaluation framework inspired by the previous work on $εδ$-almost safe set quantification. The proposal characterizes the complete safety performance of the test subject in the car-following regime. The performance of the proposed method is also demonstrated in challenging cases including some widely adopted car-following decision-making modules and the commercially available Openpilot driving stack by CommaAI.
