Scenario-based testing is considered state-of-the-art for verifying and validating Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). However, the practical application of scenario-based testing requires an efficient method to generate or collect the scenarios that are needed for the safety assessment. In this paper, we propose Goal-conditioned Scenario Generation (GOOSE), a goal-conditioned reinforcement learning (RL) approach that automatically generates safety-critical scenarios to challenge ADASs or ADSs. In order to simultaneously set up and optimize scenarios, we propose to control vehicle trajectories at the scenario level. Each step in the RL framework corresponds to a scenario simulation. We use Non-Uniform Rational B-Splines (NURBS) for trajectory modeling. To guide the goal-conditioned agent, we formulate test-specific, constraint-based goals inspired by the OpenScenario Domain Specific Language(DSL). Through experiments conducted on multiple pre-crash scenarios derived from UN Regulation No. 157 for Active Lane Keeping Systems (ALKS), we demonstrate the effectiveness of GOOSE in generating scenarios that lead to safety-critical events.

翻译：基于场景的测试被认为是验证高级驾驶辅助系统（ADAS）和自动驾驶系统（ADS）的先进方法。然而，场景测试的实际应用需要一种高效的方法来生成或收集安全评估所需的场景。本文提出目标条件场景生成（GOOSE），一种基于目标条件强化学习（RL）的方法，可自动生成挑战ADAS或ADS的安全关键场景。为了同时设置和优化场景，我们提出在场景层面控制车辆轨迹。强化学习框架中的每一步对应一个场景仿真。我们采用非均匀有理B样条（NURBS）进行轨迹建模。为了引导目标条件智能体，我们借鉴OpenScenario领域特定语言（DSL）的思想，制定了基于约束的、测试专用的目标。通过在基于联合国第157号法规中主动车道保持系统（ALKS）的多个预碰撞场景上进行实验，我们证明了GOOSE在生成导致安全关键事件场景方面的有效性。