NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
This work addresses the need for improved safety testing in autonomous driving, particularly for end-to-end planners, by providing a publicly available simulator and evaluation suite, though it is incremental as it builds on existing NeRF and simulation methods.
The authors tackled the problem of testing autonomous driving software in safety-critical scenarios by developing a NeRF-based simulator that creates photorealistic closed-loop environments, and found that state-of-the-art end-to-end planners fail critically in these scenarios despite excelling in nominal open-loop settings.
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