Driver's mental workload estimation based on empirical physiological indicators

Abstract

In order to explore driver's mental workload under different traffic situations, the field experiment aimed at giving quantitative assessments of driver's mental workload within different maneuvers by physiological indicators, such as ECG and EDA. Moreover, the driving safety assessments in different mental workload were analyzed. Firstly, the HRV was extracted from the ECG signal de-noised and filtered. And then the AVHR, SDNN, RMSSD, PNN50 and LF/HF of HRV in time-frequency were analyzed respectively. Consequently, the results showed that the driver's AVHR, SDNN, RMSSD, PNN50 and LF/HF of HRV increased obviously in left-turning maneuver when compared with those in straight-going maneuver, and there was no significant physiological difference between those in right-turning maneuver and straight-going maneuver. The inference was driver's mental workload was the highest when taking a left-turn. Furthermore, the physiological index indicated the mental workload was higher in night driving than that in daytime driving. Conclusion is helpful for the development of driving assistance based on physiological information detection.