Pedestrian Crossing Intention Prediction Based on Cross-Modal Motion Query and Global–Local Context Co-Learning for Autonomous Driving

Ensuring safety in autonomous driving requires accurate pedestrian intention prediction in complex urban environments. Nevertheless, pedestrian motion is inherently influenced by environmental factors and visual appearance, which poses a significant challenge. Although existing multi-modal methods integrate motion data with both global and local visual cues during training, applying the same model structure for inference results in high computational cost and reduces real-time performance. To address this limitation, we propose the Global-Local Context Co-learning (GLCCL) framework, which leverages both local and global context during training for collaborative learning while relying solely on lightweight motion data for efficient real-time inference. Our framework integrates three key components: a Cross-modal Query Module (CQM) for modeling vehicle-pedestrian motion feature, a Local Perception Module (LPM) for extracting local pedestrian features, and a Global Guidance Module (GGM) for encoding global traffic environment descriptions. Collaboratively training these modules enables our framework to enrich motion representations with global-local contextual information. We further visualize the CQM’s cross-attention matrices to interpret the interactions of multi-modal motion features. Experiments on the public PIE and JAAD benchmarks demonstrate that, compared with state-of-the-art multi-modal models, GLCCL achieves competitive performance while offering superior real-time capabilities and lower computational cost. Extensive ablation studies confirm the framework’s compatibility with diverse sequence-learning methods.