Temporal Spatial Multi-Scale Compressor Trajectory Prediction Network

Precise prediction of surrounding-agent trajectories is a cornerstone for ensuring safety and efficiency in autonomous driving. Yet, as traffic density and scene complexity grow, Transformer-based models - with their quadratic attention cost - struggle to operate in real time without sacrificing accuracy. To bridge this gap, we introduce Temporal-Spatial Multiscale Compression (TSMC), a novel framework that hierarchically compresses input features across both temporal and spatial dimensions, effectively 'zooming out' on less critical interactions while retaining full resolution for salient ones. By reducing the number of tokens processed at each stage, TSMC lowers the computational load when handling large numbers of traffic participants and complex contexts. We integrate TSMC into several state-of-the-art trajectory predictors and evaluate on two popular benchmarks (Argoverse I and Argoverse II). Results show consistent gains in prediction accuracy and a speed-up in inference, with negligible model-size increase. TSMC thus offers a flexible, plug-and-play solution for long-horizon, high-density trajectory forecasting in real-world autonomous systems.