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面向无人驾驶车辆的行车安全场模型构建方法

谢楚安 任羿 杨德真 冯强 孙博 王自力

谢楚安,任羿,杨德真,等. 面向无人驾驶车辆的行车安全场模型构建方法[J]. 北京航空航天大学学报,2024,50(4):1375-1383 doi: 10.13700/j.bh.1001-5965.2022.0462
引用本文: 谢楚安,任羿,杨德真,等. 面向无人驾驶车辆的行车安全场模型构建方法[J]. 北京航空航天大学学报,2024,50(4):1375-1383 doi: 10.13700/j.bh.1001-5965.2022.0462
XIE C A,REN Y,YANG D Z,et al. Construction method of driving safety field model for unmanned vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1375-1383 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0462
Citation: XIE C A,REN Y,YANG D Z,et al. Construction method of driving safety field model for unmanned vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1375-1383 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0462

面向无人驾驶车辆的行车安全场模型构建方法

doi: 10.13700/j.bh.1001-5965.2022.0462
基金项目: 可靠性与环境工程技术国家级重点实验室基金(6142004210108)
详细信息
    通讯作者:

    E-mail:09967@buaa.edu.cn

  • 中图分类号: X981

Construction method of driving safety field model for unmanned vehicles

Funds: National Key Laboratory of Reliability and Environmental Engineering Technology Foundation (6142004210108)
More Information
  • 摘要:

    针对全面系统评价无人驾驶车辆行车安全方法欠缺的问题,提出一种改进的无人驾驶车辆行车安全场模型。考虑无人驾驶车辆复杂道路因素,人工智能(AI)系统感知、决策、控制3大模块的特性对无人驾驶车辆行车安全的影响,基于胡克定律,建立动态势能场及安全行为场相结合的无人驾驶车辆行车安全场数学模型,以此表征道路上静止物体、运动物体与AI系统自身等因素造成的行车风险。结合典型行驶场景的行车安全分析验证所提模型的正确性和可用性。

     

  • 图 1  前后车车距与弹簧类比示意图

    Figure 1.  Analogous diagram of front and rear car distance and spring compression

    图 2  道路曲率直角坐标系示意图

    Figure 2.  Schematic diagram of road curvature rectangular coordinate system

    图 3  道路坡度示意图

    Figure 3.  Schematic diagram of road slope

    图 4  无人驾驶车辆实际轨迹及规划轨迹

    Figure 4.  Actual trajectory and planned trajectory of unmanned vehicle

    图 5  等效质量待定系数标定流程

    Figure 5.  Flow chart for calibration of equivalent mass undetermined coefficient

    图 6  公路平均车速与事故平均伤亡人数的关系

    Figure 6.  Relationship between average highway speed and average number of casualties in accidents

    图 7  无人驾驶车辆行车安全场典型场景

    Figure 7.  Typical driving safety field case of unmanned vehicles

    图 8  不同行车场景及其场强变化

    Figure 8.  Different driving case and field strength changes

    表  1  高速公路数据计算结果

    Table  1.   Calculation results of expressway data

    高速公路 平均车速/
    (km·h−1)
    亿车公里死亡人数/
    (108人·辆·km−1)
    亿车公里受伤人数/
    (108人·辆·km−1)
    亿车公里事故数/
    (108次·辆·km−1)
    N
    成渝高速 87.61 7 23 23 0.513
    石太高速 71.00 9 29 29 0.517
    广佛高速 58.13 6 21 21 0.500
    京石高速 93.00 15 47 45 0.547
    沪宁高速 79.86 6 20 21 0.486
    沈大高速 79.50 5 18 19 0.468
    京津塘高速 88.70 10 32 31 0.535
    下载: 导出CSV

    表  2  典型场景当前状态信息

    Table  2.   Typical scenario state information

    目标 $ \left( {{x_i},{y_i}} \right) $/m $ {m_i} $/
    kg
    $ {v_i} $/
    (m·s−1)
    $ {a_i} $/
    (m·s−2)
    $ {\theta _i} $/
    (°)
    目标① (7,1) 800 45 2 0
    目标② (30,4) 200 0 0 0
    目标③ (25,0) 1000 50 1.5 −25
    目标④ (40,3) 900 30 −1 2
    目标⑤ (47,2.5) 90 6 1 0
    目标⑥ (4,−3) 88 15 1 0
    目标⑦ (18,−4) 900 0 0 0
    目标⑧ (37,-2.5) 60 15 1 −10
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-08
  • 录用日期:  2022-09-11
  • 网络出版日期:  2022-11-03
  • 整期出版日期:  2024-04-29

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