English(EN) TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs

新型Transformer模型预测行人过马路意图

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了TrajFusionNet+，这是一款新基于Transformer的模型，旨在预测行人过马路意图，以用于自动驾驶汽车。该模型整合了序列和视觉轨迹数据以及场景图表示，以捕捉行人与交通元素之间的关系依赖性。TrajFusionNet+在PIE和JAAD等既有的行人过马路意图数据集上展示了改进的性能和卓越的泛化能力，优于现有方法。 AI

影响该模型可以通过改进行人检测和预测来增强自动驾驶系统的安全性和效率。

排序理由该集群包含一篇详细介绍新模型及其在基准测试中性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Fran\c{c}ois G. Landry, Moulay A. Akhloufi · 2026-09-11 04:00

TrajFusionNet+: 基于Transformer的行人过马路意图预测，融合轨迹表示与场景图

arXiv:2609.10806v1 Announce Type: new Abstract: The pedestrian crossing intention task involves predicting whether pedestrians are likely to cross the road from the point of view of an autonomous vehicle. We introduce TrajFusionNet+, a novel transformer-based model for pedestrian…

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TrajFusionNet+: 基于Transformer的行人过马路意图预测，融合轨迹表示与场景图

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