高速公路跟车情景下认知分心影响机制与识别方法

doi:10.16097/j.cnki.1009-6744.2025.01.021

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 221-230.DOI: 10.16097/j.cnki.1009-6744.2025.01.021

高速公路跟车情景下认知分心影响机制与识别方法

彭金栓，张淋俊，周磊，袁浩，任超宇，徐磊

重庆交通大学，交通运输学院，重庆400074

收稿日期:2024-11-30 修回日期:2024-12-17 接受日期:2024-12-23 出版日期:2025-02-25 发布日期:2025-02-24
作者简介:彭金栓(1982—)，男，安徽太和人，教授，博士。
基金资助:
教育部人文社会科学研究规划基金(24YJAZH110)；重庆市高校创新研究群体项目(CXQT21022)；重庆市自然科学基金(CSTB2022NSCQ-MSX0549)。

Influence Mechanisms and Identification of Cognitive Distraction of Car-following on Expressways

PENG Jinshuan, ZHANG Lingjun, ZHOU Lei,YUAN Hao, REN Chaoyu, XU Lei^*

College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Received:2024-11-30 Revised:2024-12-17 Accepted:2024-12-23 Online:2025-02-25 Published:2025-02-24
Supported by:
Humanities and Social Sciences Research Planning Fund Project of the Ministry of Education (24YJAZH110)；University Creative Research Group Project of Chongqing(CXQT21022)；Natural Science Foundation of Chongqing (CSTB2022NSCQ-MSX0549)。

摘要/Abstract

摘要： 为精细化研究认知分心对高速公路场景下驾驶人跟车行为的影响机理，设计面向不同分心次任务的模拟驾驶试验。动态采集车辆运动学特性，驾驶人操作和眼动特征参数，解析次任务状态与速度区间对跟车绩效的影响机制，构造面向不同速度区间跟车行为的认知分心状态表征参数集合。引入支持向量机、随机森林和极端梯度提升树等方法，实时识别驾驶人的认知分心状态。研究表明：沉浸式计算相较于交谈次任务会给驾驶人带来更大的认知负荷；认知分心导致驾驶人对方向盘和油门踏板的控制能力减弱，注视点更加集中于道路前方，视觉转移受到抑制；不同速度区间下，认知分心表征参数集合存在差异；极端梯度提升树模型性能优于支持向量机和随机森林；标定不同速度区间下的最佳滑动时窗宽度与滑动步长，极端梯度提升树模型在总体区间及速度区间Ⅰ([60,80)km·h-1)、Ⅱ([80,100)km·h-1)、Ⅲ([100, 120] km·h-1)下识别准确率分别达到85.98%、87.98%、88.45%、92.21%；截至风险阈值时刻，认知分心样本识别率最高可达90.0%。研究结果可为高速公路认知分心识别及预警优化设计等提供重要参考。

关键词: 交通工程, 认知分心识别, 极端梯度提升树, 高速公路跟车, 驾驶模拟, 识别时序性

Abstract: To investigate the impact of cognitive distraction on drivers' car-following behavior on expressways, this study conducted driving simulation experiment with various distraction tasks. The study dynamically collected vehicle kinematics characteristics, driver manipulation, and eye movement parameters, and analyzed the influence mechanism of the secondary task state and speed interval on car-following performance. A set of cognitive distraction state representation parameters was developed for car-following behavior in different speed intervals. Methods such as the Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were introduced to identify drivers' cognitive distraction states in real-time. The findings indicated that immersive computing imposed a higher cognitive load on drivers compared to conversational secondary tasks. Cognitive distraction reduced drivers' control over the steering wheel and throttle pedal, more focused gaze on the road ahead, and suppressed visual transfer. The cognitive distraction representation parameters varied across different speed intervals. The XGBoost model outperformed both the SVM and RF. By calibrating the optimal sliding window width and step size under different speed intervals, the XGBoost model achieved recognition accuracies of respectively 85.98%, 87.98%, 88.45%, and 92.21% for the overall interval and the speed intervals of I ([60, 80) km·h-1), II [80, 100) km·h-1), and III [100, 120] km·h-1). Up to the risk threshold moment, the recognition rate for cognitive distraction samples reached a maximum of 90%. The findings provide references for recognizing cognitive distraction and optimizing early warning systems on expressways.

Key words: traffic engineering, cognitive distraction recognition, extreme gradient boosting, expressway car following, driving simulation, identification of time-sequence characteristics

中图分类号:

U491

彭金栓, 张淋俊, 周磊, 袁浩, 任超宇, 徐磊. 高速公路跟车情景下认知分心影响机制与识别方法[J]. 交通运输系统工程与信息, 2025, 25(1): 221-230.

PENG Jinshuan, ZHANG Lingjun, ZHOU Lei, YUAN Hao, REN Chaoyu, XU Lei. Influence Mechanisms and Identification of Cognitive Distraction of Car-following on Expressways[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 221-230.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2025.01.021

http://www.tseit.org.cn/CN/Y2025/V25/I1/221

参考文献

[1]刘瑞,贺经纬,朱西产,等.基于自然驾驶数据的跟车场景潜在危险估计模型[J].东南大学学报(自然科学版), 2019, 49(4): 788-795. [LIU R, HE J W, ZHU X C, et al. Potential risk assessment model in car following based on naturalistic driving data[J]. Journal of Southeast University (Natural Science Edition), 2019, 49(4): 788 795.]

[2]KLAUER C, DINGUS T A, NEALE V L, et al. The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data [R]. Virginia, USA: National Highway Traffic Safety Administration, U.S. Department of Transportation, 2006.

[3]吕爱红,马勇,李腾飞,等.认知分心时驾驶人应激反应特征分析[J].中国安全科学学报,2018,28(7): 7-12.[LV A H, MA Y, LI T F, et al. Analysis of stress response characteristics of driver with cognitive distraction[J]. China Safety Science Journal, 2018, 28(7): 7-12.]

[4]MUHRER E, VOLLRATH M. The effect of visual and cognitive distraction on driver's anticipation in a simulated car following scenario[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2011, 14(6): 555-566.

[5]王畅,牛津,马万良,等.真实驾驶过程驾驶人分心特性个体差异[J].科学技术与工程,2024,24(24):10538-10544. [WANG C, NIU J, MA W L, et al. Individual differences of driver distraction characteristics during real driving[J]. Science Technology and Engineering, 2024, 24(24): 10538-10544.]

[6]李鹏辉,廖呈玮,郑志晓,等.认知分心对车辆跟驰过程操控安全性的影响[J].中国公路学报,2018,31(5):167-173. [LI P H, LIAO C W, ZHENG Z X, et al.Impact of cognitive distraction on vehicle control safety in car-following situation[J]. China Journal of Highway and Transport, 2018, 31(5): 167-173.]

[7]张玉婷,陈波佑,张双焱,等.抵近信控交叉口分心驾驶识别模型[J]. 交通运输系统工程与信息,2022,22(1): 217-224. [ZHANG Y T, CHEN B Y, ZHANG S Y, et al. Recognition model of distracted drivers when approaching signalized intersections[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 217-224.]

[8]杨艳群,黄永健,罗秀玲,等.认知分心情境下驾驶人应激反应行为研究[J].重庆交通大学学报(自然科学版), 2024, 43(3): 54-63, 115. [YANG Y Q, HUANG Y J, LUO X L, et al. Driver's stress response behavior in cognitive distraction condition[J]. Journal of Chongqing Jiaotong University (Natural Science), 2024, 43(3): 54 63, 115.]

[9]KOUNTOURIOTIS G K, SPYRIDAKOS P, CARSTEN O M, et al. Identifying cognitive distraction using steering wheel reversal rates[J]. Accident Analysis & Prevention, 2016, 96: 39-45.

[10] MOU L, CHANG J, ZHOU C, et al. Multimodal driver distraction detection using dual-channel network of CNN and Transformer[J]. Expert Systems with Applications, 2023, 234: 121066.

[11]文香, 邓超.基于车辆横向运行数据的遗传-支持向量机算法的分心驾驶状态判别模型[J].科学技术与工程, 2023, 23(25): 10990-10996. [WEN X, DENG C. GA SVM distracted driving state discrimination model based on vehicle lateral running data[J]. Science Technology and Engineering, 2023, 23(25): 10990-10996.]

[12] 周扬, 付锐, 袁伟,等.驾驶人认知分心识别随机森林模型研究[J]. 中国安全科学学报,2018, 28(1): 20-25.[ZHOU Y, FU R, YUAN W, et al. Research on drivers' cognitive distracted recognition model based on random forest[J]. China Safety Science Journal, 2018, 28(1): 20 25.]

[13] LIANG Y, LEE J D. A hybrid bayesian network approach to detect driver cognitive distraction[J]. Transportation Research Part C: Emerging Technologies, 2014, 38: 146 155.

[14] KAWANAKA H, MIYAJI M, BHUIYAN M S, et al. Identification of cognitive distraction using physiological features for adaptive driving safety supporting system[J]. International Journal of Vehicular Technology, 2013, 2013(1): 817179.

[15] 周扬, 付锐,刘卓凡.考虑驾驶人不同分心类型的分心识别研究[J]. 交通运输系统工程与信息,2022,22(3):132-139. [ZHOU Y, FU R, LIU Z F. Distracted driving recognition considering distraction types[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 132-139.]

[16] AHMAD N, ARVIN R, KHATTAK A J. How is the duration of distraction related to safety-critical events? Harnessing naturalistic driving data to explore the role of driving instability[J]. Journal of Safety Research, 2023, 85: 15-30

高速公路跟车情景下认知分心影响机制与识别方法

Influence Mechanisms and Identification of Cognitive Distraction of Car-following on Expressways

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张河山, 曾长坤, 王海洋, 田丰铭, 郑展骥, 张羽. 高速公路夜间养护作业区事故风险辨识及影响机理研究[J]. 交通运输系统工程与信息, 2025, 25(2): 157-168.
[2]	邓院昌, 蒋昀轩, 陶胜芹. 基于可解释集成学习的异常驾驶行为风险识别方法[J]. 交通运输系统工程与信息, 2025, 25(2): 180-189.
[3]	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.
[4]	王建军, 武京, 李景涛, 周英杰. 非公交车辆停靠对主辅式站台延误的影响分析[J]. 交通运输系统工程与信息, 2025, 25(2): 241-252.
[5]	宋现敏, 刘博, 李海涛, 湛天舒, 李世豪, 张云翔. 考虑综合效益的周期型停车预约分配模型[J]. 交通运输系统工程与信息, 2025, 25(1): 24-35.
[6]	曹丹妮, 王涛, 杨松坡, 屈云超, 吴建军. 混合流环境下瓶颈区域车道定制化速度调控研究[J]. 交通运输系统工程与信息, 2025, 25(1): 76-85.
[7]	韩烽凡, 刘爽, 朱亚迪, 杨烨, 王曦. 数据驱动的城轨车站行人仿真方法研究[J]. 交通运输系统工程与信息, 2025, 25(1): 86-91.
[8]	覃文文, 白碧璇, 韩春阳, 戢晓峰, 谷金晶, 田毕江. 山区双车道公路弯道路段小客车跟驰状态转移预测[J]. 交通运输系统工程与信息, 2025, 25(1): 92-101.
[9]	邬岚, 周佳雨, 李根. 技术标准更新视角下电动自行车事故严重程度影响因素演化分析[J]. 交通运输系统工程与信息, 2025, 25(1): 231-240.
[10]	林译峰, 温惠英. 高速公路隧道不同车辆实时跟驰风险影响因素分析[J]. 交通运输系统工程与信息, 2025, 25(1): 298-310.
[11]	尚婷, 胥浩, 梁叶, 周翱. 基于博弈论云物元的地下环道出入口视觉负荷研究[J]. 交通运输系统工程与信息, 2025, 25(1): 345-354.
[12]	卢凯, 陈志学, 林永杰, 郭海锋. 考虑跨相位冲突的环形交叉口信号周期计算方法[J]. 交通运输系统工程与信息, 2024, 24(6): 86-101.
[13]	郑建明, 华一丁, 张宇飞, 路文豪, 石佩鑫, 覃斌, 赵目龙. 考虑风险场景的驾驶员紧急制动模型研究[J]. 交通运输系统工程与信息, 2024, 24(6): 219-231.
[14]	戢晓峰, 王健, 徐迎豪, 卢梦媛, 覃文文. 基于驾驶风格的山区公路穿村镇段行车风险场灵敏度分析[J]. 交通运输系统工程与信息, 2024, 24(6): 316-325.
[15]	罗霜, 杨书群, 徐进. 小净距“隧道—互通”路段车辆行驶轨迹特征研究[J]. 交通运输系统工程与信息, 2024, 24(6): 326-338.