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This paper presents a driver status recognition method based on data fusion that changes the autonomous driving mode in our co-pilot system. Our research has the following two novelties: first, the fusion of information-based driver-status recognition between a direct method using the states of the driver's face and eyes and an indirect method of recognition based on the driver's driving patterns using vehicle information; and second, the ability to transfer from the driving mode to an autonomous mode through fusion of the information of the two methods. Four parameters are calculated in the fusion of these direct and indirect methods: the percent of eye closure, gaze direction, steering wheel angle, and vehicle speed. These parameters are combined to infer the level of drowsiness and attention dispersion of the driver. The system was tested under different circumstances for day and night driving conditions using different driving scenarios on a roadway. Our driver status recognition method utilized a smart device connected to our prototype autonomous vehicle.
Kim et al. (Wed,) studied this question.