Recognition of Pedestrians’ Street-Crossing Intentions Based on Skeleton Features

An integrated method is proposed to solve the problem of frequent conflicts between autonomous vehicles and pedestrians in the street crossing scene. The method involves pedestrian detection, tracking, and intention recognition. First, an enhanced YOLOv8 is introduced by combining the C2f_CA module to achieve accurate pedestrian detection, tracking and pose estimation. Second, a variety of intention recognition features are proposed to characterize the position and pose of pedestrians in spatial and time domains. Finally, by taking the feature data as input for the base learners, the intention classification model is proposed based on the Stacking model with SVM, KNN and random forest as the base learners and XGBoost as the meta learner. The experimental results show that the enhanced YOLOv8 improves the detection accuracy by 5.4% compared with the original model, and the intention recognition based on the Stacking model can achieve 94.0% accuracy on the JAAD dataset, which is improved by more than 3.4% compared with the existing intention recognition models. Furthermore, when different parts of a pedestrian are occluded, the accuracy of the Stacking model still reaches 65.8%–73.3%, which verifies the robustness of the proposed model. The proposed model provides reliable inputs for decision planning of autonomous vehicles, which is conducive to improving the safety of self-driving.