Predicting Driver's Work Performance in Driving Simulator Based on Physiological Indices

Developing an early warning model based on mental workload (MWL) to predict the driver's performance is critical and helpful, especially for new or less experienced drivers. This study aims to investigate the correlation between human's MWL and work performance and develop the predictive model in the driving task using driving simulator. The performance measure (number of errors), subjective rating (NASA Task Load Index) as well as six physiological indices were assessed and measured. Additionally, the group method of data handling (GMDH) was used to establish the work performance model. The results indicate that different complexity levels of driving task have a significant effect on the driver's performance, and the predictive performance model integrates different physiological measures shows the validity of the proposed model is well with R-2 = 0.781. The proposed model is expected to provide a reference value of their work performance by giving physiological indices. Based on this model, the driving lesson plans will be proposed to sustain the appropriate MWL as well as improve work performance.