How do obstacle characteristics and driver alertness affect the takeover process in conditionally automated driving?

IF 4.4 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour Pub Date : 2025-07-23 DOI:10.1016/j.trf.2025.07.018

Penghui Li , Shufen Zhu , Ni Zhang , Jianguo Gong , Xiaomeng Li , Chunjiao Dong , Xuedong Yan

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引用次数: 0

Abstract

Before the achievement of fully automated driving, drivers are still required to take control of the vehicle when necessary. The performance of this takeover process is influenced by various factors, such as the driver’s state of alertness and the characteristics of the traffic environment. This study explored how obstacle characteristics in the traffic scenario, driver alertness, and non-driving related tasks (NDRTs) affected the performance of the takeover process, particularly the situation understanding time and takeover reaction time, in conditionally automated driving. The AdVitam dataset published by Meteier et al. (2023) was used in this study, where a driving simulation experiment was conducted with 90 participants, collecting data on electrodermal activity (EDA) and subjective perceived risk in six different scenarios (Deer, Cone, Frog, Can, False Alarm 1, and False Alarm 2). A Structural Equation Model (SEM) was constructed to investigate the causal relationship among obstacle movability, obstacle inherent hazard, NDRTs, driver perceived risk, alertness prior to the takeover request, and takeover process. The results indicated that driver alertness, measured by features extracted from EDA, played a significant role in takeover reaction time. Higher alertness leaded to quicker reactions when taking over control. Furthermore, perceived risk, influenced by obstacle movability and inherent hazard, significantly mediated the relationship between obstacle characteristics and takeover reaction time. Additionally, obstacle movability affected situation understanding time directly. These findings suggest that obstacle characteristics and driver physiological signals can be combined for an accurate prediction of drivers’ situation understanding time and takeover reaction time in automated vehicles, thereby enabling adaptive adjustment of takeover warning lead time and enhancing human–machine interaction experience during automated-to-manual transition.

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障碍物特性和驾驶员警觉性如何影响条件自动驾驶的接管过程？

在实现全自动驾驶之前，驾驶员仍然需要在必要时控制车辆。这一接管过程的表现受到多种因素的影响，如驾驶员的警觉性状态和交通环境的特点。本研究探讨了交通场景中的障碍物特征、驾驶员警觉性和非驾驶相关任务（NDRTs）对条件自动驾驶接管过程的影响，特别是对情景理解时间和接管反应时间的影响。本研究使用Meteier et al.（2023）发布的AdVitam数据集，对90名参与者进行了驾驶模拟实验，收集了6种不同场景（Deer, Cone, Frog, Can， False Alarm 1和False Alarm 2）下的皮肤电活动（EDA）和主观感知风险数据。通过构建结构方程模型，探讨了障碍物可移动性、障碍物固有危险、NDRTs、驾驶员感知风险、接管请求前警觉性和接管过程之间的因果关系。结果表明，驾驶员警觉性在接管反应时间中起着重要作用。当接管控制权时，更高的警觉性会导致更快的反应。此外，感知风险在障碍可移动性和固有风险的影响下，显著调节了障碍特征与接管反应时间的关系。障碍物可移动性直接影响情境理解时间。研究结果表明，将障碍物特征与驾驶员生理信号相结合，可以准确预测自动驾驶车辆驾驶员的态势理解时间和接管反应时间，从而实现接管预警提前时间的自适应调整，增强自动-手动过渡过程中的人机交互体验。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Transportation Research Part F-Traffic Psychology and Behaviour Multiple-

CiteScore

7.60

自引率

14.60%

发文量

239

审稿时长

71 days

期刊介绍： Transportation Research Part F: Traffic Psychology and Behaviour focuses on the behavioural and psychological aspects of traffic and transport. The aim of the journal is to enhance theory development, improve the quality of empirical studies and to stimulate the application of research findings in practice. TRF provides a focus and a means of communication for the considerable amount of research activities that are now being carried out in this field. The journal provides a forum for transportation researchers, psychologists, ergonomists, engineers and policy-makers with an interest in traffic and transport psychology.