Prediction of Driver’s Attention Points Based on Attention Model

Abstract

The current intelligent driving system does not consider the selective attention mechanism of drivers, and it cannot completely replace the drivers to extract effective road information. A Driver Visual Attention Network (DVAN), which is based on deep learning attention model, is proposed in our paper, in order to solve this problem. The DVAN is aimed at extracting the key information affecting the driver’s operation by predicting the driver’s attention points. It completes the fast localization and extraction of road information that is most interesting to drivers by merging local apparent features and contextual visual information. Meanwhile, a Cross Convolutional Neural Network (C-CNN) is proposed in order to ensure the integrity of the extracted information. Here, we verify the network on the KITTI dataset, which is the largest computer vision algorithm evaluation data set in the world’s largest autonomous driving scenario. Our results show that the DVAN can quickly locate and identify the target that the driver is most interested in a picture, and the average accuracy of prediction is 96.3%. This will provide useful theoretical basis and technical methods that are related to visual perception for intelligent driving vehicles, driving training and assisted driving systems in the future.