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车路协同混合交通场景要素解析与测试案例生成

赵通 上官伟 柴琳果 郭蓬

赵通, 上官伟, 柴琳果, 郭蓬. 车路协同混合交通场景要素解析与测试案例生成[J]. 交通运输工程学报, 2022, 22(3): 263-276. doi: 10.19818/j.cnki.1671-1637.2022.03.021
引用本文: 赵通, 上官伟, 柴琳果, 郭蓬. 车路协同混合交通场景要素解析与测试案例生成[J]. 交通运输工程学报, 2022, 22(3): 263-276. doi: 10.19818/j.cnki.1671-1637.2022.03.021
ZHAO Tong, SHANGGUAN Wei, CHAI Lin-guo, GUO Peng. Scenario factor analysis and test case generation for vehicle-infrastructure cooperative mixed traffic[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 263-276. doi: 10.19818/j.cnki.1671-1637.2022.03.021
Citation: ZHAO Tong, SHANGGUAN Wei, CHAI Lin-guo, GUO Peng. Scenario factor analysis and test case generation for vehicle-infrastructure cooperative mixed traffic[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 263-276. doi: 10.19818/j.cnki.1671-1637.2022.03.021

车路协同混合交通场景要素解析与测试案例生成

doi: 10.19818/j.cnki.1671-1637.2022.03.021
基金项目: 

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

详细信息
    作者简介:

    赵通(1996-),男,甘肃白银人,北京交通大学工学博士研究生,从事车路协同系统研究

    上官伟(1979-),男,陕西乾县人,北京交通大学教授,工学博士

    通讯作者:

    上官伟(1979-),男,陕西乾县人,北京交通大学教授,工学博士

  • 中图分类号: U495

Scenario factor analysis and test case generation for vehicle-infrastructure cooperative mixed traffic

Funds: 

National Key Research and Development Program of China 2018YFB1600600

Science and Technology Research and Development Program of China Railway N2021G045

More Information
Article Text (Baidu Translation)
  • 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。

     

  • 图  1  异构车辆驾驶类型示意

    Figure  1.  Illustration of driving types of heterogeneous vehicles

    图  2  匝道合流场景要素层次模型

    Figure  2.  Hierarchical model of ramp merging scenario factors

    图  3  马氏链转移和接受概率示意

    Figure  3.  Illustration of Markov chain transfer and acceptance probabilities

    图  4  MH抽样算法流程

    Figure  4.  MH sampling algorithm flow

    图  5  复杂度与交通流流量关系

    Figure  5.  Relationship between complexity and traffic flow volume

    图  6  PICT改进算法与ET生成测试案例数对比

    Figure  6.  Comparison of numbers of test cases generated by improved PICT algorithm and ET

    图  7  测试案例复杂度占比对比

    Figure  7.  Comparison of test case complexity proportions

    图  8  CAV换道预警场景

    Figure  8.  CAV lane change warning scenario

    图  9  场景实例与测试案例风险程度对比

    Figure  9.  Comparison of risk degrees between scenario instances and test cases

    图  10  MC方法生成测试案例的分布

    Figure  10.  Distribution of test cases generated by MC method

    图  11  IS生成测试案例的分布

    Figure  11.  Distribution of test cases generated by IS

    1.  Illustration of driving types of heterogeneous vehicles

    2.  Hierarchical model of ramp merging scenario factors

    3.  Illustration of Marcov chain transfer and acceptance probabilities

    4.  MH sampling algorithm flow

    5.  Relationship between complexity and traffic flow volume

    6.  Comparison of numbers of test cases generated by improved PICT algorithm and ET

    7.  Comparison of test case complexity proportions

    8.  CAV lane change warning scenario

    9.  Comparison of risk degrees between scenario instances and test cases

    10.  Distribution of test cases generated by MC method

    11.  Distribution of test cases generated by IS

    表  1  不同测试案例生成方法的适用性对比

    Table  1.   Applicability comparison of different test case generation methods

    适用条件 复杂度激励的组合测试案例生成方法 基于复杂度聚类的蒙特卡洛测试案例生成方法
    需求导向 效率类 安全类
    车辆数 多车 单车
    场景规模 大规模 小规模
    下载: 导出CSV

    表  2  匝道合流场景要素示例

    Table  2.   Examples of ramp merging scenario factors

    场景要素 异属元要素 耦合指数
    光照环境 天气 0.000 4
    时段 早晨 0.000 4
    道路 主路 车道数 3 0.014 9
    限速/(m·s-1) 22 0.012 6
    匝道 车道数 1 0.011 3
    限速/(m·s-1) 11 0.033 7
    加速车道 车道数 1 0.008 2
    限速/(m·s-1) 22 0.003 5
    交通设施 路侧通信设备 0.014 0
    路侧控制设备 0.059 5
    异构机动车 HDV 加速度/(m·s-2) 2 0.008 6
    减速度/(m·s-2) 2 0.011 3
    CAV 加速度/(m·s-2) 2 0.010 0
    减速度/(m·s-2) 2 0.012 1
    混合交通流 流量 主路上游/(veh·h-1) 6 000 0.047 4
    匝道上游/(veh·h-1) 1 000 0.033 8
    CAV渗透率 0.4 0.080 5
    下载: 导出CSV

    表  3  测试案例复杂度分布对比

    Table  3.   Comparison of test case complexity distributions

    统计量 PICT算法 AETG算法 PICT改进算法
    最小值 0.096 3 0.128 0 0.107 2
    下四分位数值 0.146 7 0.209 5 0.323 8
    中值 0.233 9 0.244 3 0.440 5
    上四分位数值 0.356 8 0.315 7 0.503 4
    最大值 0.477 1 0.431 4 0.534 0
    下载: 导出CSV

    表  4  区间划分统计结果

    Table  4.   Interval partitioning statistical results

    对象类型 区间数
    单个区间内场景数 合计
    [1, 5) [5, 10) [10, 20) [20, +∞)
    场景实例 62 35 31 36 164
    测试案例 104 41 30 33 208
    下载: 导出CSV

    表  5  不同类型场景测试案例数及占比

    Table  5.   Numbers and proportions of test cases for different types of scenarios

    场景类型 方法类型 ΔtT/s 数量 占总数比例/%
    危险场景 MC (0, 0.9] 20 0.20
    IS 59 0.59
    高风险场景 MC (0.9, 1.9] 443 4.43
    IS 969 9.69
    中风险场景 MC (1.9, 2.9] 1 402 13.02
    IS 2 627 26.27
    低风险场景 MC (2.9, 20] 4 475 44.75
    IS 2 951 29.51
    下载: 导出CSV

    表  6  高危风险场景的估计概率和相对误差

    Table  6.   Estimated probabilities and relative errors for high-risk and hazardous scenarios

    场景类型 方法类型 估计概率 相对误差/%
    危险场景 MC 0.001 9 14.68
    IS 0.001 8 8.73
    高风险场景 MC 0.042 8 8.74
    IS 0.043 1 3.96
    下载: 导出CSV

    1.   Applicability comparison of different test case generation methods

    2.   Examples of ramp merging scenario factors

    3.   Comparison of test case complexity distributions

    4.   Interval partitioning statistical results

    5.   Number and proportions of test cases for different types of scenarios

    6.   Estimated probabilities and relative errors for high-risk scenarios and dangerous scenarios

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  • 收稿日期:  2021-12-12
  • 刊出日期:  2022-06-25

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