Driver Workload Characteristics Analysis Using EEG Data From an Urban Road

Abstract

The main cause of traffic accidents is drivers' human errors such as cognitive, judgment, and execution errors. To mitigate drivers' human errors, research on the measurement and quantification of driver workload as well as the development of smart vehicles is needed. Drivers' behavior while driving includes driving straight, turning left or right, U-turns, rapid acceleration, rapid deceleration, and changing lanes. To measure and quantify a driving workload, both the subjective workload and the behavior workload caused by varied driving behaviors should be taken into account on the basis of understanding the visual, auditory, cognitive, and psychomotor characteristics of the driving workload. In this paper, we analyze electroencephalogram (EEG) data collected through an urban road driving test. To overcome large deviations of EEG values among drivers, we used EEG variation rates instead of raw EEG values. We extracted five kinds of behavior sections from the data: left-turn section, right-turn section, rapid-acceleration section, rapid-deceleration section, and lane-change section. We then selected a reference section for each of these behavior sections and compared EEG values from the behavior sections with those from the reference sections to calculate the EEG variation rates, after which we made the statistical analysis. The analysis results of this study are being used to explain the cognitive characteristics of a driving workload caused by drivers' behavior in the vehicle information system, which will provide information for safe driving by taking into account the driving workload.

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