| 摘要: |
| 随着自动驾驶应用的快速普及, 其安全性问题成为学术界及工业界共同关注的焦点. 针对自动驾驶系统(autonomous driving system, ADS)的测试是解决该问题的有效手段. 目前, 主流测试方法是基于驾驶场景的仿真测试, 即通过模拟各种场景元素, 如道路、行人等, 评估待测ADS的决策. 然而, 现有方法多聚焦于关键驾驶场景的构建与动态生成, 忽视了车辆自身配置变化, 如车重、扭矩等, 对部署于其上的ADS的决策影响. 针对该问题, 基于课题组前期工作SAFEVAR, 提出安全攸关的车辆配置高效搜索方法SAFEVCS. SAFEVAR采用搜索算法, 探索暴露ADS安全隐患的车辆配置设置(VCS); 为提高搜索结果的多样性, SAFEVCS引入模糊测试, 改进搜索算法交叉与变异算子的条件限定及约束; 为提高搜索效率, SAFEVCS进一步结合车辆动力学知识, 实现搜索终止策略和去重策略的自适应. 为评估SAFEVCS的有效性及执行效率, 以SAFEVAR为对比基线, 在3个驾驶场景下进行大规模实验. 实验结果表明, SAFEVCS生成的VCS能够有效暴露ADS安全隐患. 在晴天、雨天两种天气条件下, 行人横穿马路的仿真场景中, SAFEVCS搜索到的解集能够显著降低ADS的安全表现, 且在相同的实验环境下, 仿真效率提升近2.5倍. |
| 关键词: ADS安全 仿真测试 模糊测试 搜索算法 车辆配置 |
| DOI：10.13328/j.cnki.jos.007566 |
| 分类号:TP311 |
| 基金项目: |
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| Search for Safety-critical Vehicle Configurations with Fuzzing Testing |
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WANG Tie-Xin1, MA Jian-Wei1, LIN Cong1, YANG Ke1, WANG Fei2
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1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;2.School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
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| Abstract: |
| As autonomous driving applications are rapidly popularized, their safety has become the common focus of both academia and industry. Autonomous driving system (ADS) testing is an effective means for solving this problem. Currently, the mainstream testing method is the scenario-based simulation test, which evaluates the decision of ADS to be measured by simulating various elements of driving scenarios, such as roads and pedestrians. However, existing methods mainly focus on the construction and dynamic generation of critical driving scenarios, neglecting the influence of configuration changes of the vehicle itself, such as its weight and torque, on the decision-making of ADS deployed on the vehicle. To address this issue, based on the previous work SAFEVAR, this study proposes SAFEVCS, an efficient search method for safety-critical vehicle configurations. SAFEVCS employs a search algorithm to explore the vehicle configuration setting (VCS) that exposes safety vulnerabilities of ADS. Furthermore, to improve the diversity of the search results, SAFEVCS introduces fuzzing to optimize the conditions and constraints of crossover and mutation operators in search algorithms. To improve search efficiency, SAFEVCS further combines the vehicle dynamics knowledge, which achieves the self-adaption of search termination strategy and deduplication strategy. To evaluate the effectiveness and execution efficiency of SAFEVCS, the study takes SAFEVAR as the baseline for comparison and carries out extensive experiments under three driving scenarios. The experimental results show that VCS generated by SAFEVCS can effectively expose the safety vulnerabilities of ADS. In the two weather conditions of sunny and rainy days, under the simulation scenarios of pedestrians crossing the road, the obtained solution set significantly decreased the safety performance of the ADS under test, and under the same experiment environment, the simulation efficiency is increased by approximately 2.5 times. |
| Key words: autonomous driving system (ADS) safety simulation testing fuzzing testing search algorithm vehicle configuration |
