It is important to build a rigorous verification and validation (V&V) process to evaluate the safety of highly automated vehicles (HAVs) before their wide deployment on public roads. In this paper, we propose an interaction-aware framework for HAV safety evaluation which is suitable for some highly-interactive driving scenarios including highway merging, roundabout entering, etc. Contrary to existing approaches where the primary other vehicle (POV) takes predetermined maneuvers, we model the POV as a game-theoretic agent. To capture a wide variety of interactions between the POV and the vehicle under test (VUT), we characterize the interactive behavior using level-k game theory and social value orientation and train a diverse set of POVs using reinforcement learning. Moreover, we propose an adaptive test case sampling scheme based on the Gaussian process regression technique to generate customized and diverse challenging cases. The highway merging is used as the example scenario. We found the proposed method is able to capture a wide range of POV behaviors and achieve better coverage of the failure modes of the VUT compared with other evaluation approaches.

中文翻译：

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高度自动化车辆的交互感知评估方法

建立严格的验证和确认（V＆V）流程以评估高度自动化的车辆（HAV）在公共道路上广泛部署之前的安全性，这一点很重要。在本文中，我们提出了一种用于HAV安全评估的交互感知框架，该框架适用于某些高度交互的驾驶场景，包括高速公路合并，回旋处进入等。与现有的方法相反，现有的方法是其他主要车辆（POV）采取预定的操作，我们将POV建模为博弈论主体。为了捕获POV与被测车辆（VUT）之间的各种交互，我们使用k级博弈论和社会价值取向来表征交互行为，并使用强化学习来训练各种POV。而且，我们提出了一种基于高斯过程回归技术的自适应测试用例采样方案，以生成定制的和多样化的挑战性案例。以高速公路合并为例。我们发现，与其他评估方法相比，该方法能够捕获广泛的POV行为，并更好地覆盖VUT的故障模式。