驾驶负荷生理指标度量方法

Method for Physiological Index Measurement on Driving Mental Workload

为探究驾驶负荷领域研究现状,通过Web of Science核心合集数据库获取千余篇英文文献,并利用文献计量分析中的映射知识域(MKD)方法对该领域文献进行梳理与分析。同时对生理指标测量技术在驾驶负荷领域的应用进行分类阐述及效果评价。结果表明:在各类生理指标中心电数据的应用最为广泛;融合多模态生理数据是近年来主要的研究手段,可以提升驾驶负荷的评估效果;未来研究应结合自动驾驶技术的普及与发展,关注车辆自动化水平对驾驶负荷的影响,拓展更多的研究场景,融合多源数据并从不同角度对驾驶负荷的产生机理、影响因素、量化方法进行深入研究。

To explore the study status in the field of driving load,more than 1000 English literatures were obtained through the Web of Science core collection database.The literatures in this field were sorted out and analyzed by using the mapping knowledge domain(MKD)method in bibliometric analysis.Furthermore,this study categorized and expounded upon the utilization of physiological measurements in the context of assessing driving mental workload.The applications of physiological index measurement technology were classified and evaluated in the field of driving load.Review on the literature suggests that the electrocardiography(ECG)is the most prevalent among physiological indexes.The main study method is the integration of multimodal physiological data recently,which can enhance the assessment effectiveness of driving mental workload.In future study,it is essential to integrate the popularization and development of autonomous driving technology.The influence of vehicle automation level on driving load is taken into consideration.The range of study scenarios is expanded.The multi-source data are integrated.The mechanism,influencing factors and quantitative methods for driving load are studied from different aspects.