摘要
自动驾驶中极端的场景、无法预测的人类行为等长尾问题逐渐成为制约自动驾驶系统(autonomous driving system,ADS)发展的关键要素,因此有效地生成安全关键场景对于提高自动驾驶系统的安全性至关重要.现有的自动驾驶场景生成主要依赖于大量的路采数据,采用数据驱动式场景生成方法,并结合场景泛化技术生成相应的驾驶场景.该方法耗时耗力,成本高,而且难以有效生成边缘场景.而模型驱动式场景建模方法通过构建逻辑场景模型,能够建模复杂的场景特征并有效生成安全关键场景.其面临的挑战性问题是如何设计一种基于领域知识的可视化场景建模语言,支持抽象建模复杂驾驶场景,并进一步有效地挖掘安全攸关的边缘关键场景.针对以上问题,提出一种基于SML4ADS2.0的自动驾驶场景建模及边缘关键场景生成方法.该方法基于ADS领域本体建模场景,并结合形式化量化评估与重要性采样实现边缘关键场景生成.首先,提出基于SML4ADS2.0模型驱动式场景建模方法,设计一种基于ADS领域本体的自动驾驶场景建模语言(scenario modeling language for autonomous driving system,SML4ADS2.0),以构建自动驾驶场景模型;其次,通过模型转换规则实现场景模型到随机混成自动机(stochastic hybrid automata,SHA)的模型转换,并使用模型检测工具UPPAAL-SMC对场景模型进行量化评估分析;然后,通过重要性采样技术在场景空间中快速检测到边缘场景,实现逻辑场景到边缘关键具体场景的有效生成;最后,结合变道超车等典型场景,进行案例展示.实验结果表明该方法能够有效建模场景,并解决ADS安全关键场景生成问题.
modeling of complex driving scenarios and further explores edge-critical scenarios.To address these issues,this study proposes an approach for scenario modeling with SML4ADS2.0 and edge-critical scenario generation for autonomous driving systems.This method utilizes ontology-based modeling of scenarios within the ADS domain,combining formal quantitative evaluation with importance sampling to generate edge-critical scenarios.First,a model-driven scenario modeling method based on SML4ADS2.0 is proposed,using this language to construct models of autonomous driving scenarios.Second,the conversion of scenario models to stochastic hybrid automata(SHA)is implemented through model transformation rules and the model checking tool UPPAAL-SMC is used for quantitative evaluation and analysis of the scenario models.Subsequently,importance sampling techniques are employed to rapidly detect edge scenarios within the scenario space,effectively generating specific edge-critical scenarios from logical models.Finally,the method is demonstrated through case studies involving typical scenarios such as lane changes and overtaking.Experimental results indicate that this approach can effectively model scenarios and address the generation of safety-critical scenarios for ADS.
作者
杜德慧
叶振
郑成行
朱珍珍
李家蕴
DU De-Hui;YE Zhen;ZHENG Cheng-Hang;ZHU Zhen-Zhen;LI Jia-Yun(Software Engineering Institute,East China Normal University,Shanghai 200062,China;Shanghai Key Laboratory of Trustworthy Computing(East China Normal University),Shanghai 200062,China;MOE International Joint Lab of Trustworthy Software(East China Normal University),Shanghai 200062,China)
出处
《软件学报》
北大核心
2025年第8期3512-3530,共19页
Journal of Software
基金
国家自然科学基金面上项目(62272165)。
