  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
