Understanding drivers’ situation awareness in highly automated driving using SAGAT, SART, and eye-tracking data

IF 4.4 2区 工程技术 Q1 PSYCHOLOGY, APPLIED Transportation Research Part F-Traffic Psychology and Behaviour Pub Date : 2025-02-01 Epub Date: 2025-02-07 DOI:10.1016/j.trf.2025.02.003
Young Woo Kim , Sol Hee Yoon
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Abstract

Rapid and accurately forming situation awareness (SA) is essential when transitioning from autonomous to manual driving. This study examines how drivers’ SA is developed when a takeover request (TOR) is issued and compares SA levels across different environmental and time conditions. A laboratory experiment with 39 participants was performed to analyze the SA, perceived safety, and gaze behavior with different levels of traffic density (high and low), road type (urban and highway), and time budget factor (3 s, 10 s, free). The free time budget factor was determined by assessing the participants’ response times indicating when they perceived having acquired sufficient SA. The results revealed significant effects of traffic density, road type, and time budget on the Situation Awareness Global Assessment Technique (SAGAT), Situational Awareness Rating Technique (SART), and perceived safety. Post-hoc results for time budget factors revealed significant differences based on the SAGAT score, with the 10-second condition exhibiting the highest score. For the SART and perceived safety scores, the 3-second condition was significantly lower whereas no significant difference was observed between the free and 10-second conditions and the mean response time for the free condition ranged from 10–13 s, with the longest duration in high-traffic and urban conditions. Participants gazed at different AOI when provided with a longer lead time whereas in short lead time conditions, their gaze primarily focused on the front window. These results suggest that drivers develop SA differently based on time constraints, and environmental factors. This study provides valuable insights for developing and implementing autonomous systems, contributing to safer and more efficient transitions between automated and manual driving modes.
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通过SAGAT、SART和眼动追踪数据了解高度自动驾驶中驾驶员的态势感知
在从自动驾驶向手动驾驶过渡的过程中,快速准确地形成态势感知(SA)至关重要。本研究考察了司机的SA是如何在接收请求(TOR)发出时发展起来的,并比较了不同环境和时间条件下的SA水平。采用室内实验对39名被试在不同交通密度(高、低)、道路类型(城市和高速公路)和时间预算因子(3秒、10秒、自由)下的SA、感知安全性和凝视行为进行了分析。自由时间预算因素是通过评估参与者的反应时间来确定的,这表明他们认为何时获得了足够的SA。结果表明,交通密度、道路类型和时间预算对态势感知全局评估技术(SAGAT)、态势感知评级技术(SART)和感知安全性有显著影响。时间预算因素的事后结果显示了基于SAGAT分数的显著差异,10秒条件表现出最高的分数。在SART和感知安全得分方面,3秒条件显著低于10秒条件,而自由条件和10秒条件之间无显著差异,自由条件的平均反应时间在10-13秒之间,其中交通繁忙和城市条件的持续时间最长。在提前时间较长的条件下,参与者注视着不同的AOI,而在提前时间较短的条件下,他们的目光主要集中在前窗。这些结果表明,驾驶员在时间约束和环境因素的影响下会产生不同的SA。这项研究为开发和实施自动驾驶系统提供了有价值的见解,有助于在自动驾驶模式和手动驾驶模式之间实现更安全、更有效的转换。
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来源期刊
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.
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