English(EN) STC-MGNet: Spatial-Temporal-Channel Attention-Enhanced Multimodal Graph Network for Pedestrian Intention Prediction

新型STC-MGNet改进了自动驾驶中的行人意图预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了STC-MGNet，这是一种新颖的多模态图网络，旨在改进自动驾驶系统的行人意图预测。该网络利用空间-时间-通道注意力机制来更好地解释行人姿态序列，并将视觉数据与自车运动信息相结合。实验表明，STC-MGNet在关键数据集上取得了高精度，优于现有方法。 AI

影响通过改进行人行为预测，该模型可以提高自动驾驶系统的安全性和可靠性。

排序理由该集群描述了一篇关于用于特定AI任务的新颖模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Yuanzhe Li, Meng Wang · 2026-10-19 04:00

STC-MGNet：用于行人意图预测的空间-时间-通道注意力增强多模态图网络

arXiv:2610.23356v1 Announce Type: new Abstract: Accurate prediction of pedestrian crossing intentions plays a critical role in enhancing the safety of pedestrian-vehicle interactions in autonomous driving. However, effectively capturing subtle spatiotemporal patterns in pedestria…