摘要
面向混合交通环境下多车效率类和单车安全类场景测试需求,研究了基于混合交通场景要素解析的车路协同测试案例生成方法;为提高测试案例的多样性和覆盖度,分析了混合交通特征要素相互作用机理,构建了混合交通场景要素层次模型,提出了场景要素重要度的一致性描述指标,并在此基础上建立了测试案例复杂度模型;针对多车效率类场景仿真测试,提出了复杂度激励的组合测试案例生成方法,设计了场景要素强耦合组合策略;针对单车安全类场景仿真测试,提出了基于复杂度聚类的蒙特卡洛测试案例生成方法,设计了风险场景特征参数抽样机制;选取车路协同混合交通典型场景开展仿真试验,验证了提出的测试案例生成方法的有效性。研究结果表明,对于多车效率类混合交通高速公路匝道合流场景测试,提出的方法比传统成对测试方法的场景最大复杂度提高了11.93%,高复杂度场景占比提高了60.02%,测试案例覆盖度提高了12.08%;对于单车安全类车路协同换道预警场景测试,提出的方法比传统蒙特卡洛测试方法的危险场景数提高了195%,且其参数估计误差降低了5.95%,高风险场景数提高了119%,且其参数估计误差降低了4.78%。可见,提出的方法能够提高测试案例的多样性和覆盖度,有助于开展复杂环境和风险条件下车路协同系统功能测试,能够有效满足多车效率类和单车安全类场景测试需求。
Considering the test requirements of multi-vehicle efficiency and single-vehicle safety scenarios in a mixed traffic environment, a test case generation method for the vehicle-infrastructure cooperation was developed based on the scenario factor analysis of mixed traffic. For higher diversity and coverage of test cases, the interaction mechanism of mixed traffic characteristic factors was analyzed, the hierarchical model of mixed traffic scenario factors was constructed, and the consistency description index of the importance of scenario factors was proposed. On this basis, a complexity model of test cases was built. For the simulation and test of multi-vehicle efficiency scenarios, a complexity-inspired generation combination test case method was proposed, and a combination strategy with strong coupling of scenario factors was designed. For the simulation and test of single-vehicle safety scenarios, a Monte Carlo test case generation method based on the complexity clustering was put forward, and a sampling mechanism of characteristic parameters of risk scenarios was designed. Typical scenarios of vehicle-infrastructure cooperative mixed traffic were selected for simulation experiments, to verify the effectiveness of the proposed test case generation method. Research results show that for the ramp-merging scenario test of expressways in mixed traffic of multi-vehicle efficiency, compared with the traditional pairwise test method, the proposed method improves the maximum complexity of scenarios, proportion of high-complexity scenarios, and coverage of test cases by 11.93%, 60.02%, and 12.08%, respectively. For the vehicle-infrastructure cooperative lane-changing warning scenario test of single-vehicle safety, compared with the traditional Monte Carlo test method, the proposed method raises the number of dangerous scenarios by 195%, with reducing the parameter estimation error by 5.95%, and increases the number of high-risk scenarios by 119%, with reducing the parameter estimation error by 4.78%. Therefore, the pro
作者
赵通
上官伟
柴琳果
郭蓬
ZHAO Tong;SHANGGUAN Wei;CHAI Lin-guo;GUO Peng(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China;CATARC(Tianjin)Automotive Engineering Research Institute Co.,Ltd.,Tianjin 300300,China)
出处
《交通运输工程学报》
EI
CSCD
北大核心
2022年第3期263-276,共14页
Journal of Traffic and Transportation Engineering
基金
国家重点研发计划(2018YFB1600600)
中国国家铁路集团有限公司科技研究开发计划(N2021G045)。
关键词
智能交通
车路协同
混合交通
场景要素解析
测试案例生成
组合测试
蒙特卡洛测试
intelligent transportation
vehicle-infrastructure cooperation
mixed traffic
scenario factor analysis
test case generation
combination test
Monte Carlo test
