Applying qualitative reasoning to a driver's cognitive mental load

Abstract

In this paper, we explore applying qualitative reasoning to a driver's mental state in real driving situations so as to develop a working load for intelligent transportation systems. We identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the driver's cognitive state, we will measure eye movements during car-driving situations. We can acquire data for the various actions of a car driver, in particular braking, acceleration, and steering angles from our experiment car. We constructed a driver cognitive mental load using the framework of qualitative reasoning. We checked the response of our model by qualitative simulation. We also verified the model using real data collected by driving an actual car. The results indicated that our model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations.