旁车切入下驾驶人短时响应行为异质性建模与分析

doi:10.16097/j.cnki.1009-6744.2025.02.018

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (2): 190-200.DOI: 10.16097/j.cnki.1009-6744.2025.02.018

旁车切入下驾驶人短时响应行为异质性建模与分析

巩喆¹，杨轸^*1，郑瑞平¹，袁方²

1. 同济大学，道路与交通工程教育部重点实验室，上海201804；2.江苏狄诺尼信息技术有限责任公司，南京210014

收稿日期:2024-12-27 修回日期:2025-02-15 接受日期:2025-02-25 出版日期:2025-04-25 发布日期:2025-04-20
作者简介:巩喆(1996—)，女，陕西商洛人，博士生。
基金资助:
国家自然科学基金(52372336)。

Modeling andAnalysis of Driver's Short-term Response Behavior Heterogeneity Under Cut-in Scenarios

GONG Zhe¹,YANG Zhen^*1,ZHENG Ruiping¹,YUAN Fang²

1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China; 2. Jiangsu Delauney Information Co. Ltd, Nanjing 210014, China

Received:2024-12-27 Revised:2025-02-15 Accepted:2025-02-25 Online:2025-04-25 Published:2025-04-20
Supported by:
National Natural Science Foundation of China(52372336)。

摘要/Abstract

摘要： 旁车切入是一种常见且存在潜在风险的驾驶场景，尤其当前车以低速和小间距切入时，后车驾驶人可能被迫采取紧急制动等行为，影响交通流的稳定性和安全性。针对这一问题，本文通过驾驶模拟实验收集了360例不同切入速度和间距的前车切入工况数据，分析驾驶人短时响应行为及其影响因素。在此基础上，构建一个考虑异质性和参数间相关性的相关随机参数有序Probit模型，用以识别影响后车驾驶人响应决策的关键因素。结果表明：平均边际效应显示，相比于前车无切入工况，前车以50m和100m间距切入时，后车制动概率分别增加47%和33%；以60km·h-1和80km·h-1速度切入时，制动概率分别增加31%和24%。相反，两个随机变量(后车驾驶人经验丰富和切入之前后车低速行驶)分别使后车驾驶人的制动概率降低6%和29%，加速度噪声大这一变量显著影响了这两个随机参数的均值。与传统模型相比，相关随机参数模型揭示了随机参数间的相关性，并识别出随机参数所捕获的未观测异质性显著抑制了后车驾驶人的制动决策概率。研究成果揭示了后车驾驶人在旁车切入场景中的行为特征，可为此类场景中的自动驾驶系统控制策略开发及交通安全管理提供理论依据和数据支持。

关键词: 交通工程, 驾驶行为异质性, 相关随机参数有序Probit模型, 旁车切入工况, 驾驶决策

Abstract: Cut-in scenarios are common in traffic flow and pose risks to driving safety, especially when the lead vehicle cuts in at low speeds and short distances, which may force the following driver to take emergency braking or other actions, affecting the stability and safety of traffic flow. To address this issue, this study collected data from 360 driving simulation experiments under different lead vehicle cut-in speeds and distances, and analyzed the short-term response behavior of drivers and its influencing factors. A correlated random parameters ordered Probit model considering heterogeneity and parameter correlation was developed to identify the key factors affecting the decision-making of the following driver. The results show that the average marginal effects indicate that when the lead vehicle cuts in at distances of 50 m and 100 m, the probability of the following vehicle braking increases by 47% and 33%, respectively. Similarly, when the lead vehicle cuts in at speeds of 60 km · h^-1 and 80 km · h^-1, the probability of braking increases by 31% and 24%. In contrast, two random variables (experienced following drivers and the low speed of the following vehicle before the cut-in) reduce the braking probability by 6% and 29%, respectively. In addition, the variable of high acceleration noise significantly affects the means of these two random parameters. Compared to traditional models, the correlated random parameters model reveals the correlation among random parameters and identifies that the unobserved heterogeneity captured by these parameters significantly reduces the following driver's braking decision. The findings of this study reveal the driving characteristics of following drivers in cut-in scenarios and provide both theoretical foundations and data support for control strategies in autonomous driving systems and for traffic safety management in such scenarios.

Key words: traffic engineering, driving behavior heterogeneity, correlated random parameter ordered Probit model, cut-in scenario, driving decision

中图分类号:

U491.2+53

巩喆, 杨轸, 郑瑞平, 袁方. 旁车切入下驾驶人短时响应行为异质性建模与分析[J]. 交通运输系统工程与信息, 2025, 25(2): 190-200.

GONG Zhe, YANG Zhen, ZHENG Ruiping, YUAN Fang. Modeling andAnalysis of Driver's Short-term Response Behavior Heterogeneity Under Cut-in Scenarios[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 190-200.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2025.02.018

http://www.tseit.org.cn/CN/Y2025/V25/I2/190

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旁车切入下驾驶人短时响应行为异质性建模与分析

Modeling andAnalysis of Driver's Short-term Response Behavior Heterogeneity Under Cut-in Scenarios

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