Multimodal Detection of Drivers Drowsiness and Distraction

Considering the ever-growing presence of automobiles around the world, ensuring the safety of those on and near roadways is of great importance. From the causes of accidents, drowsiness and distractedness are among the most consequential. In this paper, we use a multimodal dataset consisting of 11 recorded channels over 45 subjects to model driver’s drowsiness and distraction. Our work puts forward the application of this dataset by using segmented windows as features, resulting in four main contributions. We explore the performance of each individual modality and specify which signals and features have a better capability of detecting drowsiness and different kinds of distractions. In addition, we analyze the effects of early fusion on the classification of the driver’s state using multiple physiological and thermal channels. Finally, we use cascaded late fusion and test three voting strategies to evaluate the performance of our proposed approach. Our results confirm the effectiveness of utilizing a multimodal approach in detecting both drowsiness and distraction as two separate factors influencing the driver and provide guidelines on which signals are appropriate for detecting different driver’s states.