The scenario-based testing of operational vehicle safety presents a set of principal other vehicle (POV) trajectories that seek to force the subject vehicle (SV) into a certain safety-critical situation. Current scenarios are mostly (i) statistics-driven: inspired by human driver crash data, (ii) deterministic: POV trajectories are pre-determined and are independent of SV responses, and (iii) overly simplified: defined over a finite set of actions performed at the abstracted motion planning level. Such scenario-based testing (i) lacks severity guarantees, (ii) is easy for SV to game the test with intelligent driving policies, and (iii) is inefficient in producing safety-critical instances with limited and expensive testing effort. In this paper, the authors propose a model-driven online feedback control policy for multiple POVs which propagates efficient adversarial trajectories while respecting traffic rules and other concerns formulated as an admissible state-action space. The proposed approach is formulated in an anchor-template hierarchy structure, with the template model planning inducing a theoretical SV capturing guarantee under standard assumptions. The planned adversarial trajectory is then tracked by a lower-level controller applied to the full-system or the anchor model. The effectiveness of the proposed methodology is illustrated through various simulated examples with the SV controlled by either parameterized self-driving policies or human drivers.

中文翻译：

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运行车辆安全在线对抗测试的建模方法

操作车辆安全的基于场景的测试提出了一组主要的其他车辆 (POV) 轨迹，这些轨迹试图迫使目标车辆 (SV) 进入某个安全关键情况。目前的场景主要是 (i) 统计驱动：受人类驾驶员碰撞数据的启发，(ii) 确定性：POV 轨迹是预先确定的，独立于 SV 响应，以及 (iii) 过度简化：定义在一组有限的动作上在抽象的运动规划级别执行。这种基于场景的测试 (i) 缺乏严重性保证，(ii) SV 很容易利用智能驾驶策略进行测试，以及 (iii) 在测试工作有限且成本高昂的情况下，在生成安全关键实例方面效率低下。在本文中，作者为多个 POV 提出了一种模型驱动的在线反馈控制策略，该策略传播有效的对抗性轨迹，同时尊重交通规则和其他被制定为可接受状态-动作空间的问题。所提出的方法是在锚模板层次结构中制定的，模板模型规划在标准假设下引入了理论上的 SV 捕获保证。然后由应用于全系统或锚模型的低级控制器跟踪计划的对抗性轨迹。通过各种模拟示例说明了所提出方法的有效性，其中 SV 由参数化自动驾驶策略或人类驾驶员控制。然后由应用于全系统或锚模型的低级控制器跟踪计划的对抗性轨迹。通过各种模拟示例说明了所提出方法的有效性，其中 SV 由参数化自动驾驶策略或人类驾驶员控制。然后由应用于全系统或锚模型的低级控制器跟踪计划的对抗性轨迹。通过各种模拟示例说明了所提出方法的有效性，其中 SV 由参数化自动驾驶策略或人类驾驶员控制。