First-principles Based 3D Virtual Simulation Testing for Discovering SOTIF Corner Cases of Autonomous Driving

• URL: http://arxiv.org/abs/2401.11876v1
• Date: Mon, 22 Jan 2024 12:02:32 GMT
• Title: First-principles Based 3D Virtual Simulation Testing for Discovering SOTIF Corner Cases of Autonomous Driving
• Authors: Lehang Li, Haokuan Wu, Botao Yao, Tianyu He, Shuohan Huang, Chuanyi Liu
• Abstract summary: This paper proposes a first-principles based sensor modeling and environment interaction scheme, and integrates it into CARLA simulator. A meta-heuristic algorithm is designed based on several empirical insights, which guide both seed scenarios and mutations. Under identical simulation setups, our algorithm discovers about four times as many corner cases as compared to state-of-the-art work.
• Score: 5.582213904792781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D virtual simulation, which generates diversified test scenarios and tests full-stack of Autonomous Driving Systems (ADSes) modules dynamically as a whole, is a promising approach for Safety of The Intended Functionality (SOTIF) ADS testing. However, as different configurations of a test scenario will affect the sensor perceptions and environment interaction, e.g. light pulses emitted by the LiDAR sensor will undergo backscattering and attenuation, which is usually overlooked by existing works, leading to false positives or wrong results. Moreover, the input space of an ADS is extremely large, with infinite number of possible initial scenarios and mutations, along both temporal and spatial domains. This paper proposes a first-principles based sensor modeling and environment interaction scheme, and integrates it into CARLA simulator. With this scheme, a long-overlooked category of adverse weather related corner cases are discovered, along with their root causes. Moreover, a meta-heuristic algorithm is designed based on several empirical insights, which guide both seed scenarios and mutations, significantly reducing the search dimensions of scenarios and enhancing the efficiency of corner case identification. Experimental results show that under identical simulation setups, our algorithm discovers about four times as many corner cases as compared to state-of-the-art work.

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