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

• URL: http://arxiv.org/abs/2603.28091v1
• Date: Mon, 30 Mar 2026 06:47:19 GMT
• Title: SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting
• Abstract summary: We propose a novel streaming-based motion forecasting framework that explicitly focuses on evolving scenes.<n>Our method incrementally processes incoming observation windows and leverages an instance-aware context streaming to maintain and update latent agent representations.<n>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.
• Score: 53.74101174559609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 on the single-agent benchmarks. 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.

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