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（AD）软件系统，其设计重点在于传感器逼真度的闭环评估以及安全关键场景的生成。该仿真器从真实世界驾驶传感器数据序列中学习，并支持对新出现的未见场景进行重构与渲染。在本研究中，我们利用该仿真器测试AD模型对欧洲新车评估计划（Euro NCAP）启发的安全关键场景的响应。评估结果表明，尽管最先进的端到端规划器在开环设置的标称驾驶场景中表现优异，但在闭环设置下应对安全关键场景时暴露出严重缺陷。这凸显了端到端规划器在安全性与实际可用性方面亟需改进。通过将我们的仿真器与场景以易于运行的评估套件形式公开发布，我们诚邀研究界在受控、高度可配置且具有挑战性的传感器逼真环境中探索、优化并验证其AD模型。代码与说明详见https://github.com/atonderski/neuro-ncap