集成技术2024，Vol.13Issue(2)：15-28,14.DOI:10.12146/j.issn.2095-3135.20230726001
自动驾驶汽车的高效对抗性场景测试方法研究
Efficient Adversarial Scenario Test for Autonomous Vehicles
摘要
Abstract
In the field of autonomous driving safety research and application,the limitations of limited testing mileage and exposure to only a single hazardous scenario hinder the improvement of autonomous driving safety performance.To address these issues,testing with adversarial scenarios is considered crucial.However,existing studies utilize generic optimization algorithms as frameworks,resulting in a wastage of computational resources in exploring the parameter space,thereby leading to low efficiency.Moreover,under the constraint of computational cost,these algorithms may not be able to test a sufficient number of diverse failure samples,especially in complex environments.Adversarial scenario testing in complex environments faces three major challenges:information scarcity,sparse distribution of adversarial samples in a vast parameter space,and the difficulty in balancing exploration and exploitation during the search process.To tackle these challenges,this paper proposes an efficient framework for adversarial scenario testing.This framework employs a surrogate model to gather more information about the parameter space,selects small samples to overcome the sparse event constraints in the vast space,and focuses on the unknown regions and adversarial samples for targeted search and update,thereby achieving a balance between exploration and exploitation.Experimental results demonstrate that the proposed method in this paper exhibits a search efficiency four times higher than random sampling and more than double the efficiency compared to general genetic algorithms.Additionally,with a limited number of simulation test runs,it generates a greater number of adversarial test cases that are likely to cause the tested autonomous driving system to fail.Notably,the proposed method can identify many outlier adversarial samples,unveiling failure modes that existing algorithms fail to recognize.Furthermore,the proposed method can swiftly and comprehensively identify the vulnerable scenarios of the tested algorithm,providing support for the testing,validation,and iterative upgrade of autonomous driving algorithms.关键词
自动驾驶/安全验证/场景测试/代理模型/智能优化算法/Kriging模型Key words
autonomous driving/safety test and validation/scenario-based test/meta model/intelligent optimization algorithms/Kriging model分类
信息技术与安全科学引用本文复制引用
桑明,蒋拯民,李慧云..自动驾驶汽车的高效对抗性场景测试方法研究[J].集成技术,2024,13(2):15-28,14.基金项目
深圳市基础研究重点项目(JCYJ20200109115414354,JCYJ20200109115403807) （JCYJ20200109115414354,JCYJ20200109115403807）
广东省基金项目(2020B515130004,2023A1515011813) This work is supported by Shenzhen Basic Key Research Project(JCYJ20200109115414354,JCYJ20200109115403807)and Foundation of Guangdong Province of China(2020B515130004,2023A1515011813) （2020B515130004,2023A1515011813）
