We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfigurations and renderings of new, unseen scenarios. In this work, we use our simulator to test the responses of AD models to safety-critical scenarios inspired by the European New Car Assessment Programme (Euro NCAP). Our evaluation reveals that, while state-of-the-art end-to-end planners excel in nominal driving scenarios in an open-loop setting, they exhibit critical flaws when navigating our safety-critical scenarios in a closed-loop setting. This highlights the need for advancements in the safety and real-world usability of end-to-end planners. By publicly releasing our simulator and scenarios as an easy-to-run evaluation suite, we invite the research community to explore, refine, and validate their AD models in controlled, yet highly configurable and challenging sensor-realistic environments. Code and instructions can be found at https://github.com/atonderski/neuro-ncap

翻译：我们提出了一种基于NeRF的多功能仿真器，用于测试自动驾驶软件系统，其设计重点在于传感器逼真的闭环评估和关键安全场景的创建。该仿真器从真实驾驶传感器数据序列中学习，并能够实现新场景的重构与渲染。在本工作中，我们利用该仿真器测试自动驾驶模型对受欧洲新车评价规程（Euro NCAP）启发的关键安全场景的响应。评估结果表明，尽管最先进的端到端规划器在开环环境下的标称驾驶场景中表现出色，但在闭环环境下处理我们的关键安全场景时存在严重缺陷。这凸显了提升端到端规划器安全性与实际可用性的迫切需求。通过将仿真器与场景作为易于运行的评估套件公开发布，我们邀请研究社区在可控、高度可配置且具有挑战性的传感器逼真环境中探索、优化和验证其自动驾驶模型。代码与使用说明详见https://github.com/atonderski/neuro-ncap。