A major challenge for autonomous vehicles is interacting with other traffic participants safely and smoothly. A promising approach to handle such traffic interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle's actions, based on a driver model. However, the predictive validity of driver models used in IACs is rarely validated, which can limit the interactive capabilities of IACs outside the simple simulated environments in which they are demonstrated. In this paper, we argue that besides evaluating the interactive capabilities of IACs, their underlying driver models should be validated on natural human driving behavior. We propose a workflow for this validation that includes scenario-based data extraction and a two-stage (tactical/operational) evaluation procedure based on human factors literature. We demonstrate this workflow in a case study on an inverse-reinforcement-learning-based driver model replicated from an existing IAC. This model only showed the correct tactical behavior in 40% of the predictions. The model's operational behavior was inconsistent with observed human behavior. The case study illustrates that a principled evaluation workflow is useful and needed. We believe that our workflow will support the development of appropriate driver models for future automated vehicles.
翻译:自主车辆所面临的一个重大挑战是与其他交通参与者安全而顺利地互动。处理这种交通互动的一个很有希望的方法是用互动觉悟控制器(IACs)为自主车辆配备设备。这些控制器预测人驾驶员将如何根据驱动器模型对自主车辆的行动作出反应。然而,在独立驾驶员使用的驾驶员模型的预测有效性很少得到验证,这可能会限制独立驾驶员在简单模拟环境之外的互动能力。在本文中,我们争论说,除了评估独立驾驶员的互动能力外,还应验证其基本驾驶员模型的自然载人驾驶行为。我们为这一验证建议了一个工作流程,其中包括基于情景的数据提取和基于人类因素文献的两阶段(战术/操作)评价程序。我们在一项案例研究中展示了国际驾驶员模型在现行IAC所复制的反力学习驾驶员模型之外的互动能力。这一模型仅显示40%的预测的正确战术行为。模型的操作行为与观察到的人类自然驾驶行为不一致。案例研究表明,一个有原则性的评价工作流程是有用的,需要的。我们认为,我们未来的工作流程模型将支持未来的发展。