An on-board system for detecting driver drowsiness based on multi-sensor data fusion using Dempster-Shafer theory

This paper presents a data fusion method for the on-board detection of driver drowsiness in real time. Multiple sensors including camera to capture the driver´s eye status, angle sensor to measure the driver´s steering behavior, and clock to indicate the time on task were implemented. A data fusion framework based on Dempster-Shafer theory is built for modeling and combining the pieces of evidence, and to generate an overall inference of the driver´s drowsiness level. The method has been validated in an experiment on a driving simulator. The results suggest that the data fusion process could reduce the uncertainty in the drowsiness inference and obtain a better system performance compared with any single sensor.