a College of Mechanical and Electrical Engineering , Harbin Engineering University , China.
b Wood Industry College , Vietnam National University of Forestry , Vietnam.
Int J Occup Saf Ergon. 2019 Sep;25(3):476-484. doi: 10.1080/10803548.2017.1368951. Epub 2017 Sep 15.
Developing an early warning model to predict the driver's mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers' MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver's MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver's work performance.
开发一种能够预测驾驶员精神工作负荷（MWL）的预警模型是至关重要的，尤其是对于新驾驶员或经验不足的驾驶员来说。本研究旨在探讨新驾驶员的 MWL 与其工作表现（以错误数量为指标）之间的相关性。此外，还采用了群组数据处理方法，基于主观评估（美国国家航空航天局任务负荷指数 [NASA-TLX]）和六个生理指标，建立了驾驶员 MWL 预测模型。结果表明，NASA-TLX 与错误数量呈正相关，所提出的预测模型的 值为 0.745，表明模型具有有效性。通过提供生理指标，该模型有望为新驾驶员提供其 MWL 的参考值，并且可以提出驾驶课程计划来维持适当的 MWL，从而提高驾驶员的工作表现。
