Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China.
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China.
Accid Anal Prev. 2023 Nov;192:107289. doi: 10.1016/j.aap.2023.107289. Epub 2023 Sep 9.
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.
驾驶员工作负荷（Driver Workload，DWL）是交通安全研究中需要考虑的一个重要因素。近年来，随着交通领域科学技术的快速发展，DWL 的研究重点发生了一定的转变。本研究旨在通过文献计量分析和个体批判性文献回顾来掌握 DWL 的研究现状。通过文献计量分析来描述知识结构和发展趋势，应用知识图谱方法对可用文献进行深入挖掘。研究发现，目前 DWL 的研究重点之一已经转向研究其在自动驾驶领域的应用。主观问卷和实验测试（包括模拟技术和现场研究）是分析 DWL 的主要方法。提供了对 DWL 的影响因素、测量和性能的个体批判性文献回顾。研究结果表明，DWL 受到内在因素（如年龄、气质、驾驶经验）和外在因素（如车辆、道路、任务、环境）的高度影响。学者们正在积极探索各种因素和车辆自动化水平对 DWL 的综合影响。除了分别使用主观测量或生理参数测量来评估 DWL 外，研究还开始通过结合多种测量方法来提高分类准确性。由于不同场景对应的干扰项不同，DWL 的安全阈值研究还不够充分，但预计通过同时考虑这些内在和外在的多因素建立评估模型，可以对 DWL 进行量化并找到阈值。驾驶员或车辆性能指标直接测量 DWL 存在争议，但它们适合反映不同驾驶条件下 DWL 的影响。
