Efficient Safety Testing of Autonomous Vehicles via Adaptive Search over Crash-Derived Scenarios

• URL: http://arxiv.org/abs/2508.06575v1
• Date: Thu, 07 Aug 2025 13:55:01 GMT
• Title: Efficient Safety Testing of Autonomous Vehicles via Adaptive Search over Crash-Derived Scenarios
• Authors: Rui Zhou,
• Abstract summary: This study focuses on designing an accelerated testing algorithm for AVs in safety-critical scenarios.<n>Baidu Apollo, an advanced black-box automated driving system (ADS) is integrated to control the behavior of the ego vehicle.<n> Experimental results demonstrate a significant enhancement in testing efficiency when utilizing ALVNS-SA.
• Score: 6.000851091286008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring the safety of autonomous vehicles (AVs) is paramount in their development and deployment. Safety-critical scenarios pose more severe challenges, necessitating efficient testing methods to validate AVs safety. This study focuses on designing an accelerated testing algorithm for AVs in safety-critical scenarios, enabling swift recognition of their driving capabilities. First, typical logical scenarios were extracted from real-world crashes in the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database, obtaining pre-crash features through reconstruction. Second, Baidu Apollo, an advanced black-box automated driving system (ADS) is integrated to control the behavior of the ego vehicle. Third, we proposed an adaptive large-variable neighborhood-simulated annealing algorithm (ALVNS-SA) to expedite the testing process. Experimental results demonstrate a significant enhancement in testing efficiency when utilizing ALVNS-SA. It achieves an 84.00% coverage of safety-critical scenarios, with crash scenario coverage of 96.83% and near-crash scenario coverage of 92.07%. Compared to genetic algorithm (GA), adaptive large neighborhood-simulated annealing algorithm (ALNS-SA), and random testing, ALVNS-SA exhibits substantially higher coverage in safety-critical scenarios.

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