SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting

In dynamic traffic environments, motion forecasting models must be able to accurately estimate future trajectories continuously.Streaming-based methods are a promising solution, but despite recent advances, their performance often degrades when exposed to heterogeneous observation lengths.To address this, we propose a novel streaming-based motion forecasting framework that explicitly focuses on evolving scenes.Our method incrementally processes incoming observation windows and leverages an instance-aware context streaming to maintain and update latent agent representations across inference steps.A dual training objective further enables consistent forecasting accuracy across diverse observation horizons.Extensive experiments on Argoverse 2, nuScenes, and Argoverse 1 demonstrate the robustness of our approach under evolving scene conditions and also the single-agent benchmarks.Moreover, our model achieves state-of-the-art performance in streaming inference on the Argoverse 2 multi-agent benchmark, while maintaining minimal latency, highlighting its suitability for real-world deployment.