Risk prediction model for distracted driving: Characterizing interactions of eye glances and manual sequences

IF 5.1 2区 工程技术 Q1 TRANSPORTATION Travel Behaviour and Society Pub Date : 2024-06-17 DOI:10.1016/j.tbs.2024.100851
Sixian Li, Dalin Qian, Pengcheng Li, Xinwu Yuan, Qiong Fang
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Abstract

Distracted driving substantially undermines road safety, and the frequent, low-accuracy distracted driving risk prediction alarms may detrimentally affect drivers’ judgments. The study aims to improve risk prediction accuracy by mapping the distinctive interactions of eye glances and manual sequences in distracted driving into a graph structure of nodes and edges. Distraction patterns are categorized as visual distractions (VD), manual distractions (MD), and visual-manual distractions (VMD) based on daily driving behaviours, such as eating, drinking, reaching for something, and using mobile phones. 1,806 distraction alarm records came from an active safety platform (ASP) in Beijing, covering 69 drivers from 23 hazardous materials road transport companies. The study extracts characteristics to assess distracted driving risks, including visual, manual, and driving performance features. Subsequently, unsupervised learning is used to cluster risk features into three categories (low, medium, and high), which serve as labels for the risk prediction model. In addition, each distraction alarm sequence is divided into nodes and edges of a graph. More specifically, five visual areas, forward (F), object of distraction (Dis), left window (LW), rear-view mirror (RM), center dashboard (C), as well as manual sequences, single hand (H), double hands (2H) are represented as nodes. The edges are connected in parallel (occurring simultaneously) or in series (occurring sequentially), with arrows pointing from the earlier node to the later one. Furthermore, coupled with time, global, and environmental features, a temporal graph attention network (TGAT) with integrated time functions and multi-head attention mechanisms is developed to predict distracted driving risks. The results indicated that VMD demanded more visual and manual resources and led to more high-risk alarms than VD and MD. Besides, TGAT reached a promising result, outperforming other time series methods. This study is valuable for driver distraction monitoring and driving risk assessment, significantly contributing to the enhancement of road safety.

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分心驾驶风险预测模型:瞥一眼和手动操作序列的相互作用特征
分心驾驶严重破坏了道路安全,而频繁、低准确度的分心驾驶风险预测警报可能会对驾驶员的判断产生不利影响。本研究旨在通过将分心驾驶中眼睛瞥视和手动操作序列的独特互动映射到由节点和边组成的图结构中,从而提高风险预测的准确性。根据日常驾驶行为,如吃喝、伸手拿东西和使用手机等,分心模式被分为视觉分心(VD)、手动分心(MD)和视觉-手动分心(VMD)。1,806条分心报警记录来自北京的主动安全平台(ASP),涵盖23家危险品道路运输公司的69名驾驶员。研究提取了评估分心驾驶风险的特征,包括视觉特征、手动特征和驾驶表现特征。然后,利用无监督学习将风险特征聚类为三个类别(低、中、高),作为风险预测模型的标签。此外,每个分心警报序列都被划分为图的节点和边。更具体地说,前方(F)、分心对象(Dis)、左侧车窗(LW)、后视镜(RM)、中央仪表板(C)这五个视觉区域以及单手(H)、双手(2H)这两个手动序列被表示为节点。边缘以并联(同时发生)或串联(依次发生)的方式连接,箭头从较早的节点指向较晚的节点。此外,结合时间、全局和环境特征,开发了一个具有综合时间函数和多头注意力机制的时间图注意力网络(TGAT),用于预测分心驾驶风险。结果表明,与 VD 和 MD 相比,VMD 需要更多的视觉和手动资源,并导致更多的高风险警报。此外,TGAT 取得了良好的结果,优于其他时间序列方法。这项研究对驾驶员分心监控和驾驶风险评估很有价值,将极大地促进道路安全的提高。
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来源期刊
CiteScore
9.80
自引率
7.70%
发文量
109
期刊介绍: Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.
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