Comparative Analysis of Vehicle-Based and Driver-Based Features for Driver Drowsiness Monitoring by Support Vector Machines

Driver drowsiness is a serious threat to road safety. Most driver monitoring systems (DMSs) already embedded in vehicles to detect drowsiness use vehicle-based features (i.e., measures) computed by outward-facing cameras for lane tracking or steering wheel angle sensors to analyze lane keeping and steering control behavior. Such DMSs are referred to as indirect DMSs as they monitor drowsiness indirectly through driving performance. In this work, we extend this classical technique by using a driver monitoring camera for tracking driver-based features associated with eye blinking behavior and head movements. We refer to DMSs based only on a driver monitoring camera as direct DMSs as they monitor drowsiness directly through observable driver-based behavioral cues. In this work, we conduct a comparative analysis between an indirect and direct DMS. We also combine vehicle-based and driver-based features to examine the potential of a so-called hybrid DMS. To this end, we use a database collected from 70 participants in driving simulator experiments. The comparative analysis is performed by means of the correlation-based feature selection technique and support vector machines independently. With a balanced accuracy of 87.1%, the direct DMS significantly outperforms the indirect DMS, which reaches a balanced accuracy of only 77.9%. The hybrid DMS achieves a slightly better balanced accuracy of 87.7%. This work motivates the development and use of direct or hybrid DMSs to detect driver drowsiness and increase road safety.