驾驶人跟驰避撞控制决策影响因素

祝学亮; 刘卫旗; 柴荣军

doi:10.11959/j.issn.2096-3750.2025.00463

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驾驶人跟驰避撞控制决策影响因素

理论与技术 | 更新时间：2026-01-26

- 驾驶人跟驰避撞控制决策影响因素
- Factors influencing the decision-making of drivers in car-following collision avoidance control
- 物联网学报 2025年9卷第3期页码：161-169
- 作者机构：
  
  延安大学西安创新学院，陕西西安 710100
- 作者简介：
  
  [ "祝学亮（1986‒ ），男，延安大学西安创新学院讲师，主要研究方向为车联网、汽车安全与计算机等。" ]
  [ "刘卫旗（1969‒ ），男，延安大学西安创新学院高级工程师，主要研究方向为机械设计与制造、物联网等。" ]
  [ "柴荣军（1972‒ ），男，延安大学西安创新学院工程师，主要研究方向为信息计算与智能系统、大数据等。" ]
- 基金信息：
  
  延安大学西安创新学院校级科研项目(2024XJKY16)
- DOI：10.11959/j.issn.2096-3750.2025.00463
  中图分类号： U491.2+54
- 收稿：2024-11-21，
  
  修回：2025-01-06，
  
  纸质出版：2025-09-10
- 稿件说明：
移动端阅览
祝学亮,刘卫旗,柴荣军.驾驶人跟驰避撞控制决策影响因素[J].物联网学报,2025,09(03):161-169.

ZHU Xueliang,LIU Weiqi,CHAI Rongjun.Factors influencing the decision-making of drivers in car-following collision avoidance control[J].Chinese Journal on Internet of Things,2025,09(03):161-169.
祝学亮,刘卫旗,柴荣军.驾驶人跟驰避撞控制决策影响因素[J].物联网学报,2025,09(03):161-169. DOI： 10.11959/j.issn.2096-3750.2025.00463.

ZHU Xueliang,LIU Weiqi,CHAI Rongjun.Factors influencing the decision-making of drivers in car-following collision avoidance control[J].Chinese Journal on Internet of Things,2025,09(03):161-169. DOI： 10.11959/j.issn.2096-3750.2025.00463.

摘要

为了分析影响驾驶人跟驰过程中避撞操纵行为的因素，建立了一个简化的跟驰模型，并设置了模型参数，使用雷达、视频监控系统、传感器、试验车搭建了一个试验平台。使用Matlab截取测试数据，然后使用过滤算法处理数据以获得有效数据。通过统计分析，研究驾驶个体行为指标的分布特征，发现跟车时距（THW

time headway warning）和规避碰撞时间（TTC

time to collision）这两个指标在松开油门踏板和踩下刹车踏板这两个动作时刻变化较小，说明THW和TTC比较符合驾驶人对追尾风险的主观判断。分析40名样本的驾驶行为THW和TTC参数分布特征，发现个体与总体指标的分布特征基本一致，说明了不同驾驶人大都具有相似的驾驶行为。进行相关性分析，结果表明车距与两车相对速度相关程度最高，两个动作时刻的相关程度分别达到了0.81和0.76，显然TTC更加符合驾驶人对追尾风险的判断意志。分析了紧急程度对驾驶行为的影响，发现前方车辆减速越紧急跟驰车辆驾驶人的反应时间越短。天气条件也会影响驾驶人对追尾风险的判断，雨雾天气下跟车距离显著增大，表明驾驶人在天气不利的条件下会增加跟车距离以应对追尾风险。除此之外，道路条件也会影响驾驶人的跟车行为。

Abstract

In order to analyze the factors that affect the driver’s collision avoidance control behavior during the car following

a simplified car following model was set up with model parameters

and a test vehicle was built using radar and video monitoring systems and sensors for testing. Recruiting test personnel to conduct tests

Matlab was used to capture test data

and then filtering algorithms were employed to process the data in order to obtain valid data. Studying the distribution characteristics of individual driving behavior indicators

it was found that THW and TTC showed relatively small changes in the timing of releasing the accelerator pedal and pressing the brake pedal

indicating that THW and TTC were more in line with drivers’ subjective judgment of rear end collision risk. Analyzing the distribution characteristics of THW and TTC parameters in 40 samples of driving behavior

it was found that the distribution characteristics of individual and overall indicators were basically consistent

indicating that different drivers mostly had similar driving behaviors. Correlation analysis is conducted

and the results show that the distance between vehicles is most closely related to the relative speed of the two vehicles. The correlation between the two action moments reached 0.81 and 0.76

respectively

indicating that TTC is more in line with the driver’s judgment of rear end collision risk. The impact of urgency on driving behavior is analyzed

and it is found that the more urgent the deceleration of the vehicle ahead

the shorter the reaction time of the following vehicle driver. Weather conditions can also affect drivers’ judgment of rear end collision risk. In rainy and foggy weather

the following distance significantly increases

indicating that drivers will increase the following distance to cope with rear end collision risk under unfavorable weather conditions. In addition

road conditions can also affect drivers’ following behavior.

关键词

Keywords

references

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