Even as technology and performance gains are made in the sphere of automated driving, safety concerns remain. Vehicle simulation has long been seen as a tool to overcome the cost associated with a massive amount of on-road testing for development and discovery of safety critical "edge-cases". However, purely software-based vehicle models may leave a large realism gap between their real-world counterparts in terms of dynamic response, and highly realistic vehicle-in-the-loop (VIL) simulations that encapsulate a virtual world around a physical vehicle may still be quite expensive to produce and similarly time intensive as on-road testing. In this work, we demonstrate an AV simulation test bed that combines the realism of vehicle-in-the-loop (VIL) simulation with the ease of implementation of model-in-the-loop (MIL) simulation. The setup demonstrated in this work allows for response diagnosis for the VIL simulations. By observing causal links between virtual weather and lighting conditions that surround the virtual depiction of our vehicle, the vision-based perception model and controller of Openpilot, and the dynamic response of our physical vehicle under test, we can draw conclusions regarding how the perceived environment contributed to vehicle response. Conversely, we also demonstrate response prediction for the MIL setup, where the need for a physical vehicle is not required to draw richer conclusions around the impact of environmental conditions on AV performance than could be obtained with VIL simulation alone. These combine for a simulation setup with accurate real-world implications for edge-case discovery that is both cost effective and time efficient to implement.

翻译：尽管自动驾驶领域在技术和性能方面取得了进展，但安全问题依然存在。车辆仿真长期以来被视为一种工具，用于克服在开发和发现安全关键的“边缘案例”时所需的大量道路测试所带来的成本。然而，纯软件车辆模型可能在动态响应方面与其现实世界对应物之间存在较大的现实性差距，而将虚拟世界封装在实体车辆周围的高度现实性车辆在环（VIL）仿真可能仍然成本高昂，且与道路测试一样耗时。在本工作中，我们展示了一个将车辆在环（VIL）仿真的现实性与模型在环（MIL）仿真的易实现性相结合的自动驾驶汽车仿真测试平台。本工作所展示的设置允许对VIL仿真的响应进行诊断。通过观察围绕我们车辆虚拟表现的虚拟天气和光照条件、Openpilot的基于视觉的感知模型和控制器，以及被测试实体车辆的动态响应之间的因果联系，我们可以得出关于感知环境如何影响车辆响应的结论。相反，我们还展示了MIL设置的响应预测，其中不需要实体车辆即可得出比单独使用VIL仿真更丰富的关于环境条件对自动驾驶汽车性能影响的结论。这些结合构成了一个仿真设置，具有准确的现实世界意义，用于发现边缘案例，且既经济高效又省时易实现。