Key result
A hybrid Driver Monitoring System combining behavioral and physiological data showed benefits in estimating driver's drowsiness compared to single approaches during simulator driving sessions.
Why the study?
Most current Driver Monitoring Systems are passive, prompting the need for active systems that combine behavioral and physiological approaches to detect driver drowsiness.
A hybrid driver monitoring system combining behavioral and physiological data improves the estimation of driver drowsiness compared to single-modality approaches.
May enhance simulator-based drowsiness detection; leaves open real-world validation before any safety applications.
Driver Monitoring Systems are gaining increasing interest from car-makers, being featured by technologies capable to improve road safety levels and prevent accidents. Currently, these systems are mostly passive, based on distance traveled and travel time. The market trend is moving towards active systems, featured by popular approaches, such as behavioral and physiological. This article proposes a Driver Monitoring System to detect driver's drowsiness with a hybrid technique that uses both approaches. The first performs a driver's face analysis by monitoring ocular variables; the second is based on biometric signals, such as Heart Variability and Galvanic Skin Response. A distributed hardware/software architecture has been designed in order to develop real-time data acquisition and a fuzzy-based inference. Comparison tests between the hybrid approach and the two single ones showed the benefits of the proposed method in estimating driver's drowsiness, considering a subjective estimation conducted during driving sessions with the CARLA simulator.
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Montanaro et al. (2022) studied Driver's drowsiness. Hybrid Driver Monitoring System (behavioral and physiological data) vs. Single approaches (behavioral only or physiological only) was evaluated on Estimating driver's drowsiness. A hybrid Driver Monitoring System combining behavioral and physiological data showed benefits in estimating driver's drowsiness compared to single approaches during simulator driving sessions.
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