A Spatio-temporal Graph Network Allowing Incomplete Trajectory Input for Pedestrian Trajectory Prediction

2025-01-22

AI Summary

To address trajectory prediction failure caused by incomplete pedestrian historical trajectories due to occlusion and other factors, this paper proposes a robust trajectory prediction method tailored for mobile robot navigation. The method introduces a novel spatiotemporal graph neural network (ST-GNN) explicitly designed to handle missing-frame inputs. It explicitly models static obstacles as graph nodes, incorporates observation-state embeddings, and proposes a clustering-driven dynamic graph construction mechanism to enable adaptive, obstacle-aware topological modeling. Extensive experiments on multiple public benchmarks demonstrate that the proposed approach significantly outperforms state-of-the-art methods, particularly under high-occlusion conditions—achieving notable improvements in both prediction accuracy and robustness. This work provides a new paradigm for safe robot navigation in real-world complex environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Pedestrian trajectory prediction is important in the research of mobile robot navigation in environments with pedestrians. Most pedestrian trajectory prediction algorithms require the input historical trajectories to be complete. If a pedestrian is unobservable in any frame in the past, then its historical trajectory become incomplete, the algorithm will not predict its future trajectory. To address this limitation, we propose the STGN-IT, a spatio-temporal graph network allowing incomplete trajectory input, which can predict the future trajectories of pedestrians with incomplete historical trajectories. STGN-IT uses the spatio-temporal graph with an additional encoding method to represent the historical trajectories and observation states of pedestrians. Moreover, STGN-IT introduces static obstacles in the environment that may affect the future trajectories as nodes to further improve the prediction accuracy. A clustering algorithm is also applied in the construction of spatio-temporal graphs. Experiments on public datasets show that STGN-IT outperforms state of the art algorithms on these metrics.

Problem

• incomplete pedestrian trajectories
• robot navigation
• prediction algorithms

Innovation

• STGN-IT
• Temporal and Spatial Integration
• Grouping Strategy