考虑过街行人运动轨迹不确定性的人-车碰撞概率预测方法

韩勇; 谈笑天; 潘迪; 金钱钱; 李永强; 吴贺

doi:10.3963/j.jssn.1674-4861.2023.03.004

考虑过街行人运动轨迹不确定性的人-车碰撞概率预测方法

doi: 10.3963/j.jssn.1674-4861.2023.03.004

韩勇^{1, 2, ,},
谈笑天¹,
潘迪³,
金钱钱¹,
李永强¹,
吴贺³

1.
厦门理工学院机械与汽车工程学院福建厦门 361024
2.
福建省新能源汽车与安全技术研究院福建厦门 361024
3.
厦门大学航空航天学院福建厦门 361102

基金项目:

国家自然科学基金项目 51775466

厦门市自然科学基金面上项目 3502Z20227223

福建省工信厅项目 2022G043

详细信息

通讯作者:
韩勇（1984—），博士，教授. 研究方向：汽车安全. E-mail：yonghan@xmut.edu.cn

中图分类号: U471.1
计量
- 文章访问数: 595
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- PDF下载量: 55
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-15
- 网络出版日期: 2023-09-16

A Method for Predicting the Collision Probability between Crossing-street Pedestrians and Vehicles Considering the Uncertainty of Pedestrians' Movement Trajectories

1.
School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China
2.
Fujian Institute of New Energy Vehicle and Safety Technology, Xiamen 361024, Fujian, China
3.
School of Aerospace Engineering, Xiamen University, Xiamen 361102, Fujian, China

摘要

摘要: 为了准确预测人-车冲突中的碰撞风险，研究了利用碰撞概率评估人-车碰撞风险的预测方法。基于车辆运动特征建立车辆运动学模型，通过采集行人实际过街运动轨迹并提取不确定性特征，采用一阶马尔科夫模型和高斯白噪声建立行人随机运动模型，在此基础上构建人-车冲突距离模型；运用蒙特卡洛抽样，提取行人过街过程中的人-车最短距离和碰撞时间（time to collision，TTC）分布特征，通过拟合这些特征来估算最短距离和TTC的概率密度函数，建立人-车碰撞概率预测模型；结合2起人-车深度事故案例和3种不同制动特性的自动紧急制动（automatic emergency braking，AEB）系统，对比验证人-车碰撞概率预测模型的有效性。结果显示：建立的行人随机运动模型，其模拟的行人运动速度的均值和标准差与实际值的绝对误差在2%以内，模型精度较高；在事故案例仿真中，车辆与行人在发生碰撞时刻对应的碰撞概率为100%；在车辆加装AEB的仿真中，激进型AEB，法规型AEB以及保守型AEB在触发时刻对应的碰撞概率分别为超过了80%，在30%~40%之间，以及不足5%，这表明人-车碰撞概率预测模型可有效预测2起真实案例中行人和车辆在不同时刻的碰撞风险，且与使用固定触发阈值的AEB相比，建立的人-车碰撞概率预测模型能够更加准确直观地反应人-车碰撞风险。
- 交通安全 /
- 人-车碰撞 /
- 碰撞概率 /
- 蒙特卡洛 /
- 不确定性 /
- 深度事故
Abstract: In order to accurately predict collision risk in pedestrian-vehicle conflicts, a prediction method of collision probability is proposed to assess collision risk between pedestrians and vehicles. A kinematic vehicle model is established based on vehicle motion characteristics, and a stochastic kinematics model is established for pedestrians based on a first-order Markov model with Gaussian white noise by collecting pedestrians' movement trajectories of street crossings and extracting uncertainty features. Moreover, a collision distance model for pedestrian-vehicle conflicts is developed on the proposed kinetics models. The distribution of the minimum distances and time to collision (TTC) between vehicles and pedestrians during pedestrian street crossings are extracted by using a Monte Carlo sampling method. Then, a prediction model of pedestrian-vehicle collision probability is developed by feature fitting methods to estimate the probability density functions of the minimum distances and TTC. Finally, the prediction model of pedestrian-vehicle collision probability is verified based on two pedestrian-vehicle accidents and three automatic emergency braking (AEB) systems with different braking characteristics. The results show that the error of the mean and standard deviation of pedestrians' motion velocities simulated from the proposed stochastic kinematics model for pedestrians is smaller than 2%. In the simulated accident cases, the probability of the occurrence of pedestrian-vehicle collision is 100%, while for the simulated vehicles with AEB, the aggressive AEB, regulatory AEB and conservative AEB have a collision probability of more than 80%, between 30% and 40% and less than 5%, respectively. It shows that the prediction model of pedestrian-vehicle collision probability can effectively predict the collision risk between pedestrians and vehicles at different moments in the two real cases, and has a better performance than the AEB with a fixed threshold.
- traffic safety /
- pedestrian-vehicle collision /
- collision probability /
- Monte Carlo /
- uncertainty /
- in-depth accident

HTML全文

图 1 碰撞概率预测流程

Figure 1. Process of collision probability prediction

下载: 全尺寸图片幻灯片

图 2 行人过街场景

Figure 2. Scenario of pedestrians crossing the road

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图 3 车辆运动学模型

Figure 3. Vehicle kinematics model

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图 4 行人过街轨迹对比

Figure 4. Comparison of pedestrians'crossing trajectories

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图 5 行人速度分布对比

Figure 5. Comparison of pedestrians'velocity distribution

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图 6 初始仿真状态$d_{\min }$和$t_{\overline{\underline{\mathrm{TTC}}}}$分布(案例1)

Figure 6. Distribution of $d_{\min }$ and $t_{\overline{\underline{\mathrm{TTC}}}}$ at the initial simulation moment (Case 1)

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图 7 车辆制动过程

Figure 7. Process of the vehicle brake

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图 8 行人过街瞬间（案例1）

Figure 8. Crossing moment of the pedestrian(Case 1)

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图 9 人-车碰撞概率曲线（案例1）

Figure 9. Curve of pedestrian-vehicle collision probability(Case 1)

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图 10 碰撞概率与人-车相对距离及车速关系曲线（案例1）

Figure 10. Curve of relationship between collision probability and relative pedestrian-vehicle distance and vehicle velocity(Case 1)

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图 11 行人过街瞬间（案例2）

Figure 11. Crossing moment of the pedestrian(Case 2)

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图 12 人-车碰撞概率曲线（案例2）

Figure 12. Curve of pedestrian-vehicle collision probability(Case 2)

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图 13 碰撞概率与人-车相对距离及车速关系曲线（案例2）

Figure 13. Curve of relationship between collision probability and relative pedestrian-vehicle distance and vehicle velocity(Case 2)

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表 1 3种不同制动类型AEB参数设置^[26-29]

Table 1. Parameter setting for 3 different brake types of AEB

参数	激进型	法规型	保守型
a_max/g	0.9	0.9	0.5
t_trigger/s	0.8	1.1	1.4
θ_FOV/（°）	50/75	75	90
探测距离/m	60	60	60
制动器延迟时间/s	0.1	0.1	0.1

下载: 导出CSV

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