提出新框架，让交通预测模型在不同观察窗口下都保持高精度和低延迟。
SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting
- 逐帧处理观测窗口，用感知实例的上下文流更新目标状态表示
- 在Argoverse 2上达到当前最优流式推理性能，且延迟极低
- 适合真实场景部署，尤其适用于动态变化的多智能体交通环境
在动态交通环境中，运动预测模型需持续准确估计未来轨迹。基于流式的方法具有前景，但现有方法在面对异构观测长度时性能常下降。为此，我们提出一种新型流式运动预测框架，专注于动态演化场景。该方法增量式处理连续观测窗口，并利用实例感知的上下文流，在推理过程中持续维护和更新潜在的智能体表示。双重训练目标进一步确保了在不同观测时长下的稳定预测精度。在Argoverse 2、nuScenes和Argoverse 1上的大量实验表明，该方法在动态场景下表现出色，同时在单智能体基准上也表现优异。模型在Argoverse 2多智能体流式推理任务中达到最先进水平，且保持极低延迟，展现出其在实际部署中的适用性。
原文摘要 · Abstract (English)
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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