An Interaction-aware Evaluation Method for Highly Automated Vehicles
An Interaction-aware Evaluation Method for Highly Automated Vehicles
复制标题
高度自动化车辆的交互感知评估方法
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
H. Peng
中科院分区：
文献类型：
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作者：
Xinpeng Wang;Songan Zhang;Kuan;H. Peng
建立严格的验证和确认（V&V）流程，以在高度自动化车辆（HAV）在公共道路上广泛部署之前评估其安全性非常重要。在本文中，我们提出了一个互动意识的框架HAV安全评估，这是适合于一些高度互动的驾驶场景，包括高速公路合并，环形交叉口进入等现有的方法相反，主要的其他车辆（ESTA）采取预定的机动，我们建模ESTA作为一个博弈论代理。为了捕捉驾驶员和被测车辆（VUT）之间的各种交互，我们使用k级博弈理论和社会价值取向来描述交互行为，并使用强化学习来训练一组不同的POV。此外，我们提出了一个自适应的测试用例抽样方案的基础上高斯过程回归生成定制和多样化的挑战性的情况下。高速公路合并被用作示例场景。我们发现，所提出的方法是能够捕捉到广泛的故障行为，并实现更好的覆盖率的VUT的故障模式相比，其他评估方法。
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 to generate customized and diverse challenging cases. The highway merging is used as the example scenario. We found that 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.