Multimodal Polynomial Fusion for Detecting Driver Distraction
This work addresses distracted driving, a safety issue causing thousands of deaths, by improving automatic detection, though it is incremental as it builds on existing multimodal research.
The paper tackled the problem of detecting driver distraction by introducing a new multimodal dataset and using features from facial expression, speech, and car signals, resulting in superior detection accuracy with a polynomial fusion technique compared to baseline models.
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone. Although there has been a considerable amount of research on modeling the distracted behavior of drivers under various conditions, accurate automatic detection using multiple modalities and especially the contribution of using the speech modality to improve accuracy has received little attention. This paper introduces a new multimodal dataset for distracted driving behavior and discusses automatic distraction detection using features from three modalities: facial expression, speech and car signals. Detailed multimodal feature analysis shows that adding more modalities monotonically increases the predictive accuracy of the model. Finally, a simple and effective multimodal fusion technique using a polynomial fusion layer shows superior distraction detection results compared to the baseline SVM and neural network models.