计算机科学 ›› 2025, Vol. 52 ›› Issue (10): 275-286.doi: 10.11896/jsjkx.240800030
孙乐乐, 黄松, 郑长友, 夏春艳, 阳真
SUN Lele, HUANG Song, ZHENG Changyou, XIA Chunyan, YANG Zhen
摘要： 自动驾驶技术的快速发展为交通出行带来了巨大的变革潜力,但在现实交通环境中,自动驾驶车辆的安全违规行为会导致巨大的损失。为了确保自动驾驶系统能够在各种复杂的交通环境中安全运行,在部署到实际道路之前必须对其进行充分的测试。由于自动驾驶测试场景空间的复杂性和高维性,现有安全关键场景生成方法存在成本高昂、效率低等问题。因此,提出了一种基于进化搜索的自动驾驶安全关键场景生成方法——GCE3S。GCE3S将场景中的障碍物及其属性映射为基因组成的染色体结构,从而对障碍物(车辆、天气、行人等)进行更加细致的扰动,构建具有对抗性的安全关键场景,并通过多个目标函数引导进化搜索算法生成多样化的安全关键场景。此外,在工业级自动驾驶系统百度Apollo和LGSVL模拟环境中对GCE3S进行了对比实验,实验结果表明,在相同的时间内,GCE3S生成的安全关键场景数量相较于最好的基准MOSAT方法提升了20.4%,生成的安全关键场景在多样性上增加了20%。
中图分类号:
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