TransDBC: Transformer for Multivariate Time-Series based Driver Behavior Classification

Improving driving safety by monitoring driver behavior is an excellent example of Advanced Driver Assistance Systems (ADAS). This paper proposes an end-to-end transformer-based driver behavior classification framework named Trans-DBC. It calculates driver behavior from the multivariate time-series smartphone telematics data by learning short and long-range temporal dependencies effectively and accurately, unlike prior data-driven deep learning models that capture information locally via convolutional or recurrent structure in an iterative manner. The extensive human-in-the-loop study on a publicly available UAH-DriveSet dataset shows that the proposed technique can classify unsafe driving behavior with a 96% of average weighted precision, recall, F-measure, and 95.38% accuracy. Our suggested model outperforms baselines and state-of-the-art models on the UAH-DriveSet dataset and five multivariate time-series datasets in driving behavior analysis.