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

HAN Yong; TAN Xiaotian; PAN Di; JIN Qianqian; LI Yongqiang; WU He

doi:10.3963/j.jssn.1674-4861.2023.03.004

Volume 41 Issue 3

Jun. 2023

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Article Navigation > Journal of Transport Information and Safety > 2023 > 41(3): 30-40

HAN Yong, TAN Xiaotian, PAN Di, JIN Qianqian, LI Yongqiang, WU He. A Method for Predicting the Collision Probability between Crossing-street Pedestrians and Vehicles Considering the Uncertainty of Pedestrians' Movement Trajectories[J]. Journal of Transport Information and Safety, 2023, 41(3): 30-40. doi: 10.3963/j.jssn.1674-4861.2023.03.004

Citation:

HAN Yong, TAN Xiaotian, PAN Di, JIN Qianqian, LI Yongqiang, WU He. A Method for Predicting the Collision Probability between Crossing-street Pedestrians and Vehicles Considering the Uncertainty of Pedestrians' Movement Trajectories[J]. Journal of Transport Information and Safety, 2023, 41(3): 30-40. doi: 10.3963/j.jssn.1674-4861.2023.03.004

Citation:

HAN Yong, TAN Xiaotian, PAN Di, JIN Qianqian, LI Yongqiang, WU He. A Method for Predicting the Collision Probability between Crossing-street Pedestrians and Vehicles Considering the Uncertainty of Pedestrians' Movement Trajectories[J]. Journal of Transport Information and Safety, 2023, 41(3): 30-40. doi: 10.3963/j.jssn.1674-4861.2023.03.004

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

doi: 10.3963/j.jssn.1674-4861.2023.03.004

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

Received Date: 2022-11-15
Available Online: 2023-09-16

Abstract

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

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References(29)

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

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