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.

翻译：验证自动驾驶汽车在开放、动态环境中运行的安全性极具挑战性，因为车辆最终会遭遇缺乏代表性训练数据的安全关键场景。通过提高不同道路和交通条件的覆盖率，并在基于仿真的场景测试中包含边缘案例，可以提升自动驾驶汽车的安全性。然而，创建包含多个智能体的边缘案例场景并非易事。我们的方法允许工程师基于历史交通数据生成新颖、真实的边缘案例，并解释这些场景为何具有安全关键性。本文引入概率车道图来描述车辆可能行驶的一组有限车道位置和方向。概率车道图的结构直接从时空交通数据中学习得到。该图模型以概率策略的形式表示驾驶员对给定状态的响应行为。我们采用强化学习技术来调整该策略，生成可用于评估自动驾驶汽车安全性的真实且可解释的边缘案例场景。