Validating the safety of Autonomous Vehicles (AVs) operating in open-ended, dynamic environments is challenging as vehicles will eventually encounter safety-critical situations for which there is not representative training data. By increasing the coverage of different road and traffic conditions and by including corner cases in simulation-based scenario testing, the safety of AVs can be improved. However, the creation of corner case scenarios including multiple agents is non-trivial. Our approach allows engineers to generate novel, realistic corner cases based on historic traffic data and to explain why situations were safety-critical. In this paper, we introduce Probabilistic Lane Graphs (PLGs) to describe a finite set of lane positions and directions in which vehicles might travel. The structure of PLGs is learnt directly from spatio-temporal traffic data. The graph model represents the actions of the drivers in response to a given state in the form of a probabilistic policy. We use reinforcement learning techniques to modify this policy and to generate realistic and explainable corner case scenarios which can be used for assessing the safety of AVs.

自主车辆安全验证的挑战在于不同于训练数据的安全关键情况，通过基于仿真场景测试扩展道路和交通条件的涵盖范围以及包含边界情况，可以改善自主车辆的安全性。我们的方法基于历史交通数据，允许工程师生成新颖、逼真的边界情况，并解释为什么这些情况是安全关键的。我们引入了概率车道图(PLGs)来描述有限的车道位置和方向，PLGs的结构是直接从时空交通数据中学习的。这个图模型通过以概率策略的形式，表示驾驶员对给定状态的响应行为，我们使用强化学习技术修改这个策略，生成逼真且可解释的边界情况，用于评估自主车辆的安全性。

利用学习到的概率车道图生成和解释边界案例