研究人员推出 SceneSelect，这是一种新颖的以场景为中心的轨迹预测范式，解决了传统以模型为中心方法的局限性。这种新方法分析场景特征，将输入动态路由到专门的专家模型，从而提高准确性并减少计算浪费。SceneSelect 利用无监督聚类来对场景进行分类，并使用分类模块来分配输入，从而可以灵活地与现有模型集成，并在无需大量重新训练的情况下适应新数据集。实验表明，SceneSelect 在多个基准测试中的平均性能优于现有方法 10.5%。 AI
影响 通过将输入动态路由到专用模型来提高轨迹预测的准确性和效率。
排序理由 介绍轨迹预测新方法的学术论文。
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arXiv:2604.24514v1 Announce Type: new Abstract: Accurate trajectory prediction is fundamentally challenging due to high scene heterogeneity - the severe variance in motion velocity, spatial density, and interaction patterns across different real-world environments. However, most …
Accurate trajectory prediction is fundamentally challenging due to high scene heterogeneity - the severe variance in motion velocity, spatial density, and interaction patterns across different real-world environments. However, most existing approaches typically train a single uni…