NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving

• URL: http://arxiv.org/abs/2404.07762v4
• Date: Tue, 23 Apr 2024 07:29:18 GMT
• Title: NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
• Authors: William Ljungbergh, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Åström, Michael Felsberg, Christoffer Petersson,
• Abstract summary: We present a versatile NeRF-based simulator for testing autonomous driving software systems. The simulator learns from sequences of real-world driving sensor data. We use our simulator to test the responses of AD models to safety-critical scenarios inspired by Euro NCAP.
• Score: 19.709153559084093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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