Driver workload classification through neural network modeling using physiological indicators

Advanced Driver Assistance Systems may have a positive effect on traffic flow efficiency, the environment, safety and comfort. However these systems may have a negative impact on driving behavior following a change in driver workload. It is therefore crucial to develop a so-called driver workload manager. In order to manage driver workload an adequate classification of driver workload is indispensible. In this contribution we propose to classify and predict driver workload through physiological indicators of driver workload, driver characteristics and characteristics of the driving condition using a neural network modeling approach. We show that the proposed network yields a very good classification of driver workload. The contribution finishes with a discussion section and recommendations for future research.