English(EN) GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction

新的GEAR模型动态激活社会背景以改进轨迹预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了GEAR，一种新颖的人类轨迹预测模型，通过关注社会背景如何在未来轨迹生成过程中被激活来解决现有方法的局限性。与主要强调社会信息编码的先前方法不同，GEAR在每个未来步骤中动态调整个体运动和社会互动线索的贡献。这使得模型能够根据互动证据的强度和可靠性更好地控制社会因素的贡献，从而在基准数据集上获得改进的性能。 AI

影响这项研究可能为自主系统和机器人带来更准确、更具上下文感知的预测模型。

排序理由该集群包含一篇详细介绍新模型及其实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jiaheng Chen, Jiaxing Li, Leixia Wang, Jianan Ju, Tinghe Zhang · 2026-09-15 04:00

GEAR：从动态编码到动态激活在社交轨迹预测中的应用

arXiv:2609.13778v1 Announce Type: cross Abstract: Human trajectory prediction requires modeling both individual motion patterns and social interactions among agents. Existing methods have made substantial progress by using attention mechanisms, graph structures, and temporal enco…

本文如何被排名

报道来源 [1]

GEAR：从动态编码到动态激活在社交轨迹预测中的应用

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