Dual-STGAT: Dual Spatio-Temporal Graph Attention Networks With Feature Fusion for Pedestrian Crossing Intention Prediction

Abstract

Pedestrian intent prediction is critical for autonomous driving, as accurately predicting crossing intentions helps prevent collisions and ensures the safety of both pedestrians and passengers. Recent research has focused on vision-based deep neural networks for this task, but challenges remain. First, current methods suffer from low efficiency in multi-feature fusion and unreliable predictions under challenging conditions. Additionally, real-time performance is essential in practical applications, so the efficiency of the algorithm is crucial. To address these issues, we propose a novel architecture, Dual-STGAT, which uses a dual-level spatio-temporal graph network to extract pedestrian pose and scene interaction features, reducing information loss and improving feature fusion efficiency. The model captures key features of pedestrian behavior and the surrounding environment through two modules: the Pedestrian Module and the Scene Module. The Pedestrian Module extracts pedestrian motion features using a spatio-temporal graph attention network, while the Scene Module models interactions between pedestrians and surrounding objects by integrating visual, semantic, and motion information through a graph network. Extensive experiments conducted on the PIE and JAAD datasets show that Dual-STGAT achieves over 90% accuracy in pedestrian crossing intention prediction, with inference latency close to 5ms, making it well-suited for large-scale production autonomous driving systems that demand both performance and computational efficiency.