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
this https URL

我们提出了一种灵活多用途的基于 NeRF 的模拟器，用于测试自动驾驶软件系统，重点关注传感器真实感闭环评估和创建安全关键场景。通过从实际驾驶传感器数据序列中学习，模拟器能够重构和渲染新的未见场景。我们使用该模拟器测试了自动驾驶模型对欧洲新车评估计划（Euro NCAP）所启发的安全关键场景的响应。我们的评估结果表明，虽然最先进的端到端规划器在开环环境中的正常驾驶场景中表现出色，但在闭环环境中导航我们的安全关键场景时存在严重缺陷，这凸显了端到端规划器在安全性和实际可用性方面的改进需要。通过以易于运行的评估套件的形式公开发布我们的模拟器和场景，我们邀请研究界在受控但高度可配置和具有挑战性的传感器真实环境中探索、完善和验证他们的自动驾驶模型。