Egocentric Vulnerable Road Users Trajectory Prediction With Incomplete Observation

Vulnerable Road Users (VRUs) trajectory prediction aims to analyze the future movements of pedestrians and cyclists for intelligent driving. Most previous methods just focus on VRUs trajectory prediction using idealized complete observations, and rare consider occlusions and tracking losses. Focus on the incomplete observation problem, we propose a novel Observation Store and Query Fusion Network (OSQF-Net), for VRUs trajectory prediction with incomplete observation. Firstly, based on the external memory bank mechanism and complete-incomplete observation joint training strategy, a Memory Bank-based Feature Store and Query Module (MSQ-Module) is proposed to extract complete motion features, from disrupted motion patterns caused by incomplete observation. Subsequently, based on temporal extraction and attention mechanism, a Spatio-Temporal Fusion Module (STF-Module) is proposed to effectively fuse the pseudo-complete motion features and incomplete motion features in both spatial and temporal dimensions. Finally, with these two modules and a CVAE network, the OSQF-Net can generate a latent space with complete motion patterns, which guides future trajectory prediction. Experimental results demonstrate that OSQF-Net achieves superior prediction performance and real-time inference capability for egocentric VRUs trajectory prediction, under both complete and incomplete observation scenarios.