Pedestrian crossing intent inference method based on multi-feature fusion

Accurately understanding and predicting pedestrian crossing intent is crucial for ensuring the safety of autonomous vehicles. Existing approaches are often limited to visual motion cues such as pedestrian trajectories or body poses, while overlooking interactive signals like gestures and head orientations, making it difficult to capture key cues of pedestrian-vehicle interaction. To address these limitations, ARPCI (accurate reasoning for pedestrian crossing intent) was proposed, a multi-feature fusion framework designed for pedestrian intent inference. Specifically, a pedestrian feature module was developed that first focused on skeleton-based features to capture motion trends, and further leveraged MobileNet to extract head pose features. Combined with YOLOv8n for gesture recognition, pedestrian-vehicle interaction signals were captured more comprehensively by the model. In addition, a scene encoding module and a self-vehicle feature module were introduced to integrate contextual and ego-dynamic information, thereby enhancing adaptability to complex traffic environments and improving prediction accuracy. Extensive experiments on the widely used JAAD dataset show that the approach achieves an accuracy of 88%, surpassing several state-of-the-art counterparts. Moreover, the ablation studies provide further evidence of the effectiveness of the proposed input features.