考虑双重不确定性的智能车辆碰撞风险评估

doi:10.13229/j.cnki.jdxbgxb.20231290

Abstract

Abstract:

To improve the safety of intelligent vehicles， intelligent driving systems must be able to accurately predict the evolution of traffic scenarios and accurate evaluate the risk of potential collisions， especially considering the uncertainties of future vehicle status and movement. To address this problem， this paper proposes a collision risk assessment method considering dual uncertainties， including the uncertainty of vehicle multi-state perception and the uncertainty of vehicle motion model considering driver control input and road geometry factors. Firstly， the extended Kalman filter is used to complete the vehicle state estimation， integrating road geometry and driver behavior to optimize model matching. By determining the model switching threshold and allocating the yaw rate weight，the switching between diffenrent kinematic models to make the model more suitable for the current driving scenario and more accurate uncertain vehicle trajectory prediction. Secondly， the heuristic Monte Carlo simulation is used to test the potential collisions of the sampled trajectory points， and the collision risk is quantified as the probability of future collisions. The deterministic detection is used as heuristic information to significantly improve the estimation efficiency. Finally， according to the China-new car assessment program， four typical collision scenarios are built in Prescan， including rear-end collision driving in a straight line， lateral collision at an intersection， forward collision during lane change overtaking， and rear-end collision driving in a curve， for validation and assessment of the proposed method in this paper. The results show that in a variety of collision scenarios， the proposed method in thi paper can accurately perceive the risk of collision 2 seconds before the occurrence of a collision and provide a 100% collision probability warning， effectively avoiding or mitigating collisions.

Key words: intelligent driving, dual uncertainties, trajectory prediction, collision risk assessment

CLC Number:

U491

Rui ZHAO,Qi-rui YUAN,Jia-jun LIAN,Fei GAO,Hong-yu HU,Zhen-hai GAO. Intelligent vehicle collision risk assessment considering dual uncertainties[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2597-2610.

Figures/Tables 7

Fig.1

Table 1

Fig.2

Fig.3

Table 2

Initial value assignment of sensor noise， process noise and vehicle covariance matrix"

参数	数值
自车初始协方差	$∑ X E (0) = 0 8 × 8$
自车初始过程噪声	$S a E = (0.31 m / s 3) 2 s - 1, S w E = (1 . 28 ? ° / s 2) 2 s - 1$
自车传感器噪声	$[v x = 0.32, v y = 0.32, w = 0.04, a x = 0.01, a y = 0.05, w ˙ = 0.1, a ˙ x = 0.1, a ˙ y = 0.05]$
目标车初始协方差	$∑ X O (0) = d i a g$ $[0.5 ? m ? 0.5 ? m ? 0.3 ? r a d ? 0.2 m / s ? 0.2 ? m / s ? 0.2 ? r a d / s ? 0.2 ? m / s 2 ? 0.2 ? m / s 2]$
目标车初始过程噪声	$S x E = S y E = (0.5 ? m / s 2) 2 s - 1,$ $S w E = (0.262 ? r a d / s 2) 2 s - 1$
目标车传感器测量噪声	$[p x = 0.32, p y = 0.32, v x = 0.1, v y = 0.1, w = 0.04, a x = 0.1, a y = 0.05]$

Table 2

Fig.4

Fig.5

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权重	弯道半径R
权重	>50 m	20~50 m	<20 m
w₁	0.8	0.5	0.2
w	0.2	0.5	0.8

Intelligent vehicle collision risk assessment considering dual uncertainties

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