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English(EN) SceneSelect: Selective Learning for Trajectory Scene Classification and Expert Scheduling

SceneSelect 引入选择性学习用于轨迹预测，准确率提升 10.5%

作者 PulseAugur 编辑部 · [2 个来源] ·

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。

在 arXiv cs.LG 阅读 →

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SceneSelect 引入选择性学习用于轨迹预测，准确率提升 10.5%

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介绍轨迹预测新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xinrun Wang, Deshun Xia, Ke Xu, Weijie Zhu ·

    SceneSelect：用于轨迹场景分类和专家调度的选择性学习

    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 …

  2. arXiv cs.LG TIER_1 English(EN) · Weijie Zhu ·

    SceneSelect：用于轨迹场景分类和专家调度的选择性学习

    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…

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