What Causes a Driver's Attention Shift? A Driver's Attention-Guided Driving Event Recognition Model

Despite much effort to try to research driver's spatial attention allocation in driving situations, the computer vision community rarely focuses on what causes driver's attention shifts. In this paper, we built an attention-based driving event dataset (ADED) constructed from the attention distributed on the traffic participants or elements and proposed a model using driver's attention as the guidance to better recognize the events that lead driver's attention shifts. We relabeled and redivided BDD-A, a driver attention dataset in critical traffic situations, into six different semantic categories of driving events. The new dataset is introduced for driving event recognition. In addition, we proposed a special driving event recognition model (called DER-Net) with driver's attention guidance to recognize the event causes a driver's attention shift. In DER-Net, a driver's attention-guided (DAG) branch is constructed to consider the driver's spatiotemporal attention information. The proposed model achieves a superior performance compared to other state-of-the-art models in action recognition. In ablation study, many experiments are conducted to discuss proper length of the sequence to input the model, the optimal criterion to find out the frame when driver's attention shifts and the appropriate function to quantify different attention maps in neural network.