摘要
车路协同预警系统可提升雾区等不良天气条件下高速公路交通的安全性,但其容易引发驾驶人分心,因此有必要建立高速公路雾区车路协同视觉特性与驾驶行为的关联关系。本文利用驾驶模拟技术构建车路协同预警系统测试平台,获取驾驶人的眼动数据和行为数据,使用极端随机树模型对不同分类方案下的驾驶行为数据和视觉分心数据进行了分类预测,找到了驾驶行为与视觉特征的关联点。本研究可为人机交互界面的优化和分心水平的划分提供理论支撑。
Cooperative vehicle-infrastructure warning system can improve the safety of expressway traffic under adverse weather conditions such as foggy areas,but it is easy to trigger driver distraction,so it is necessary to establish the association relationship between cooperative vehicle-infrastructure visual characteristics and driving behavior in foggy areas of expressways.This paper uses driving simulation technology to build a cooperative vehicle-infrastructure warning system test platform,obtain the driver's eye movement data and behavioral data,and uses the extreme random tree model to classify and predict the driving behavioral data and visual distraction data under different classification schemes,and find the relationship points between the driving behavior and visual characteristics.This study can provide theoretical support for the optimization of human-machine interface and the classification of distraction level.
作者
郎晓礼
吴俊辉
郜义浩
裴晓栋
LANG Xiaoli;WU Junhui;GAO Yihao;PEI Xiaodong(Beijing Yunxingyu Traffic Technology Co.Ltd.,Beijing 100078,China;Beijing Boyotod Technology Co.Ltd.,Beijing 100078,China)
出处
《交通工程》
2024年第7期17-23,共7页
Journal of Transportation Engineering
关键词
高速公路
雾天
车路协同预警
视觉特性
驾驶行为
expressway
foggy weather
cooperative vehicle-infrastructure warning
visual characteristics
driving behavior
作者简介
郎晓礼(1981-),男,本科,高级工程师,研究方向为电气及其自动化。E-mail:langxiaoli@yunxingyu.com;通讯作者:裴晓栋(1988-),男,本科,研究方向为软件研发技术、项目管理。E-mail:peixiaodong@bytd.cn。
