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Using a visual attentional task and eye-tracking to investigate the effects of pre-takeover resource engagement on attention restoration in conditionally automated driving

视觉注意 任务(项目管理) 认知 眼动 情感(语言学) 资源(消歧) 认知心理学 功能(生物学) 心理学 毒物控制 计算机科学 工程类 人工智能 沟通 神经科学 医疗急救 生物 系统工程 进化生物学 医学 计算机网络
作者
Jinzhen Dou,Chang Xu,Wenyu Wu,Chengqi Xue,Shanguang Chen
出处
期刊:Traffic Injury Prevention [Taylor & Francis]
卷期号:26 (5): 506-514 被引量:2
标识
DOI:10.1080/15389588.2024.2427865
摘要

OBJECTIVE: Attention forms the foundation for the formation of situation awareness. Low situation awareness can lead to driving performance decline, which can be dangerous in driving. The goal of this study is to investigate how different types of pre-takeover tasks, involving cognitive, visual and physical resources engagement, as well as individual attentional function, affect driver's attention restoration in conditionally automated driving. METHODS: A two-phase study was conducted. In phase one, a visual attentional task was employed to measure the attentional function of driver. In phase two, a driving simulator experiment was conducted, where participants experienced a typical sequence of automated driving, takeover and manual driving. Three pre-takeover tasks were designed to divert drivers' attentional resources, including a visual-cognitive task, a visual-physical task, and a monitoring task (control group). Eye-tracking metrics, including pupil and gaze behavior, along with driving behavior, were assessed as dependent variables. RESULTS: The visual-cognitive task showed the highest percentage of pupil dilation and significantly increased participant's response time, but it also had a positive effect on subsequent attention restoration. Moreover, the attentional task scores were positively correlated with horizontal gaze scanning and negatively correlated with takeover response time. CONCLUSIONS: Pre-takeover tasks with cognitive resource engagement proves to be superior for attention restoration in conditionally automated driving. The drivers with better attentional function are able to reduce recovering time. These findings make it possible to predict drivers' attentional state by identifying type of pre-takeover tasks in conditionally automated vehicles. Based on this, the attentive user interfaces could be adaptively adjusted to provide valuable cues, ensuring a safe transition.
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