# Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding Source: [Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding]() Collection index: [llms.txt]() Data updated: 2026-01-01T14:40:23.112Z Authors: Ruochen Li, Stamos Katsigiannis, Hubert P. H. Shum Published: 2022-06-30T13:28:53.000Z ## Abstract Trajectory prediction of road users in real-world scenarios is challenging because their movement patterns are stochastic and complex. Previous pedestrian-oriented works have been successful in modelling the complex interactions among pedestrians, but fail in predicting trajectories when other types of road users are involved (e.g., cars, cyclists, etc.), because they ignore user types. Although a few recent works construct densely connected graphs with user label information, they suffer from superfluous spatial interactions and temporal dependencies. To address these issues, we propose Multiclass-SGCN, a sparse graph convolution network based approach for multi-class trajectory prediction that takes into consideration velocity and agent label information and uses a novel interaction mask to adaptively decide the spatial and temporal connections of agents based on their interaction scores. The proposed approach significantly outperformed state-of-the-art approaches on the Stanford Drone Dataset, providing more realistic and plausible trajectory predictions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Trajectory Prediction on Stanford Drone (test)](): Trajectory Prediction; Stanford Drone (test); minADE (20): not recorded. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()