FHPE-Net: Pedestrian Intention Prediction Using Fusion with Head Pose Estimation Based on RNN

Abstract

Accurate real-time prediction of pedestrian crossing intent during the autonomous driving process is crucial for ensuring the safety of both pedestrians and passengers, as well as improving riding comfort. However, existing methods for pedestrian crossing intent detection mostly rely on extracting complete pose information of pedestrians, leading to reduced accuracy when pedestrians are occluded. To address this issue, this paper proposes FHPE-Net: a lightweight, multi-branch prediction model that utilizes only the head pose features of pedestrians. In pedestrian crossing scenarios, pedestrian behavior is highly influenced by surrounding vehicles and the environment. FHPE-Net encodes pedestrian head poses and global context semantic image sequences to comprehensively capture spatiotemporal interaction features between pedestrians, vehicles, and the environment, thereby enhancing the accuracy of pedestrian crossing intent prediction. To improve the robustness of the FHPE-Net method, this study further processes bounding box positions and vehicle velocity features, making it more stable and reliable in complex traffic scenarios. Finally, a novel U-BiGRUs module is introduced for feature fusion, and an optimal fusion strategy is employed to achieve the best predictive performance in terms of F1 score and accuracy (ACC). Extensive ablation experiments are conducted on the PIE dataset, and performance analysis demonstrates that FHPE-Net achieves an accuracy of 90%, outperforming baseline methods such as PCPA and Multi-RNN, while using only pedestrian head pose features. This research holds significant guidance in enhancing traffic safety and optimizing urban traffic management. Furthermore, it provides essential technological support for advancing the commercialization of autonomous driving.