Object-Level Attention Prediction for Drivers in the Information-Rich Traffic Environment

An object-level attention prediction framework for drivers in the urban environment with rich semantic and motion information is proposed in this article. The proposed framework is based on the visual working memory mechanism, which decomposes the perception process into three phases, external stimuli, cognitive constructing, and memory search. In the external stimuli phase, semantic and motion information of surrounding objects is obtained. In the cognitive constructing phase, the neighbor-based hierarchical clustering method is applied to extract both independent and dependent features of traffic participants and driving events. In the memory search phase, the heterogeneous motif graph neural network is utilized to construct visual memory layers and integrate multilevel features for attention reasoning. Finally, the feature embedding is fed into a multilayer perceptron to predict the object-level visual attention. Training and testing data are collected from crowded and dynamic traffic scenes. Experimental results show that the proposed framework can achieve a superior object-level prediction performance in the information-rich environments compared with the state-of-the-art methods. In addition, the proposed framework can reduce the time bias of visual attention effectively.