Jun Li#,*, Kai Xu#,*, Baozhu Chen, Xiaohan Yang, Mengting Sun, Guojun Li, HaoJie Du
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3349-3368, 2025, DOI:10.32604/cmc.2025.067316
- 23 September 2025
Abstract Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving, social robotics, and intelligent surveillance systems. Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents. These interactions are critical to trajectory prediction accuracy. While prior studies have employed Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) to model such interactions, these methods fail to distinguish varying influence levels among neighboring pedestrians. To address this, we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions. Specifically,… More >
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