NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
<|reference_start|>NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving: 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<|reference_end|>
@article{ljungbergh2024neuroncap:,
    title={NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous
  Driving},
    author={William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar,
  Kalle {AA}str"om, Michael Felsberg and Christoffer Petersson},
    journal={arXiv preprint arXiv:2404.07762},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07762},
    primaryClass={cs.CV}
}