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

摘要/Abstract

摘要：

为了提高智能车辆的安全性，智能驾驶系统必须能够精准预测交通场景的演变以及准确评估潜在碰撞的风险，尤其是考虑到未来车辆状态和运动不确定性。针对这一问题，本文提出了一种考虑双重不确定性的碰撞风险评估方法，涵盖车辆多状态感知不确定性以及考虑驾驶员控制输入与道路几何因素的车辆运动模型不确定性。首先，采用扩展卡尔曼滤波器完成车辆状态估计，结合道路几何与驾驶员行为优化匹配运动模型，通过模型切换阈值确定以及横摆角速度权重分配实现了不同运动学模型切换，以使得模型更适配当前驾驶场景，进行更精准的不确定性车辆轨迹预测。其次，通过启发式蒙特卡洛模拟对采样轨迹点进行潜在碰撞的检验，并将碰撞风险量化为未来碰撞概率，其中确定性检测作为启发式信息以显著提高估计效率。最后，依据中国新车评价规程，在Prescan中搭建了4种典型碰撞场景，包括直线行驶追尾碰撞、交叉路口侧向碰撞、变道超车正向碰撞和曲线行驶追尾碰撞，用于验证和评估本文所提方法。结果表明，在多种碰撞场景中，本文提出的方法能够在车辆发生碰撞前2 s准确感知风险，并提供100%的碰撞概率预警，有效避免或减轻碰撞。

关键词: 智能驾驶, 双重不确定性, 轨迹预测, 碰撞风险评估

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

中图分类号:

U491

赵睿,袁其瑞,连家俊,高菲,胡宏宇,高镇海. 考虑双重不确定性的智能车辆碰撞风险评估[J]. 吉林大学学报(工学版), 2025, 55(8): 2597-2610.

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.

图/表 7

图1

表1

图2

图3

表2

传感器噪声、过程噪声以及车辆协方差矩阵初始值赋予"

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	$[p x = 0.32, p y = 0.32, v x = 0.1, v y = 0.1, w = 0.04, a x = 0.1, a y = 0.05]$

表2

图4

图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