SHARP: 短時間ウィンドウストリーミングによる動作予測の正確かつ堅牢な予測
SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting
動的交通環境での動作予測において、観測長の不均一性による性能劣化を解決するため、進化するシーンに焦点を当てたストリーミングベースの動作予測フレームワークを提案した。
詳しい要約
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著者: Alexander Prutsch, Christian Fruhwirth-Reisinger, David Schinagl, Horst Possegger
分類: cs.CV, cs.RO
原文アブストラクト
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.
