A systematic review on the influence factors, measurement, and effect of driver workload

工作量 自动化 领域(数学) 计算机科学 光学(聚焦) 抓住 可靠性工程 工程类 数学 机械工程 操作系统 光学 物理 程序设计语言 纯数学
作者
Jun Ma,Yiping Wu,Jian Rong,Xiaohua Zhao
出处
期刊:Accident Analysis & Prevention [Elsevier BV]
卷期号:192: 107289-107289 被引量:37
标识
DOI:10.1016/j.aap.2023.107289
摘要

Driver workload (DWL) is an important factor that needs to be considered in the study of traffic safety. The research focus on DWL has undergone certain shifts with the rapid development of scientific and technological advancements in the field of transportation in recent years. This study aims to grasp the state of research on DWL by both bibliometric analysis and individual critical literature review. The knowledge structure and development trend are described using bibliometric analysis. The knowledge mapping method is applied to mine the available literature in depth. It is discovered that one of the current research focus on DWL has shifted towards investigating its application in the field of autonomous driving. Subjective questionnaires and experimental tests (including both simulation technology and field study) are the main approaches to analyze DWL. An individual critical literature review of the influencing factors, measurement, and performance of DWL is provided. Research findings have shown that DWL was highly impacted by both intrinsic (e.g., age, temperament, driving experience) and external factors (e.g., vehicles, roads, tasks, environments). Scholars are actively exploring the combined effects of various factors and the level of vehicle automation on DWL. In addition to assess DWL by using subjective measures or physiological parameter measures separately, studies have started to improve classification accuracy by combining multiple measurement methods. Safety thresholds of DWL are not sufficiently studied due to the various interference items corresponding to different scenarios, but it is expected to quantify the DWL and find the threshold by establishing assessment models considering these intrinsic and external multiple-factors simultaneously. Driver or vehicle performance indicators are controversial to measure DWL directly, but they were suitable to reflect the impact of DWL in different driving conditions.
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