基于图表示的智能车行人意图识别方法

The problem of pedestrian-vehicle conflict in intelligent driving scenes is closely related to pedestrian crossing behavior. In order to enable advanced driving assistance system (ADAS) to have the function of identifying pedestrian crossing intentions and raising advanced warning of pedestrian-vehicle collision events, a pedestrian crossing intention recognition framework based on graph representation learning (GRL) method is proposed. It uses open source tools to generate pedestrian skeleton information. Then it establishes a graph model to represent the characteristics of pedestrian action sequence by taking the skeleton key points of each frame of pedestrian within a sequence as nodes, as well as taking the natural connections, the topological correlations and time-domain relationships between skeleton joints as edges. Taking the graph structure data as the input, the pedestrian crossing intention recognition model is trained based on support vector machine (SVM). The results show that the classification accuracy of pedestrian crossing intention can reach 90.29%. The proposed method can effectively identify the pedestrian crossing intention, which is of great significance to improve the safety of intelligent vehicle decision-making.