AS-Fuzzer: An Optimized ADS Fuzzing Method via Scenario Segmentation and Parallel Evolution

Autonomous Driving Systems (ADS) hold significant potential for enhancing travel convenience. Ensuring the reliability of ADS through efficient and comprehensive simulation testing has garnered substantial attention from researchers. In recent years, various search-based automated test scenario generation methods have been proposed to identify potential ADS defects. However, these methods still face challenges in balancing efficiency with comprehensive testing and suffer from a lack of diversity. To address these challenges, we propose AS-Fuzzer, an optimized ADS fuzzing method based on composite traffic scenario generation. AS- Fuzzer introduces a scenario slicing technique based on traffic road structures, allowing each sliced scenario to evolve independently and in parallel, enhancing interaction rates and balancing efficiency with comprehensive testing. A novel scenario generation method Co-Evolutionary Genetic Algorithms (CEGA), is applied within AS-Fuzzer to improve the diversity of generated scenarios, thereby exploring a wider range of ADS defects. Experimental results demonstrate that the proposed method improves test scenario generation efficiency by 120% compared to the state-of-the-art baseline method. Additionally, in the simulation testing of the proposed method, the interaction rate between the ADS vehicle and non-player characters is 2.79 times that of the baseline method, thereby further enhancing ADS testing efficiency. Furthermore, within the same time frame, the proposed method uncovered 19 types of ADS defects that other baseline methods did not explore, achieving higher ADS defect diversity.