CGVA: Cognitive Guided Visual Attention Selection in Traffic Driving Environment

The visual attention of human drivers in traffic scenarios is often affected by subjective and objective factors. Unfortunately, the previous work on visual attention prediction of drivers rarely considered the impact of subjective intention on the selection of focus area and object. Therefore, we propose a cognitively guided modeling for the visual attention selection of the driver. Firstly, we use the driving experience of human drivers to construct a cognitive map that depicts the relationship between driving intention and the focus area and object in different situations (including driving scene and vehicle status). Subsequently, we combine the object information recommended by the graph in the current situation with the object information in the environment to determine the object list that needs attention. Finally, class activation mapping (CAM) is used to map the object into a heat map, which is then fused with the attention area weight to generate the final visual attention prediction. To verify the effectiveness of the method, we constructed the Intention-Related Driving Attention Dataset (IRDA) and conducted experiments. The experimental results show that our method is superior to the classic saliency prediction model and deep network-based visual attention in main indicators. In addition, we also conducted experiments on the public datasets BDDA and DADA. The experimental results show that the method of this paper obtains results that are as competitive as the state-of-the-art method.