English(EN) Multi-Context Fusion Transformer for Pedestrian Crossing Intention Prediction in Urban Environments

新型Transformer模型提升自动驾驶汽车的行人意图预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了一种多上下文融合Transformer（MFT）模型，旨在提高自动驾驶汽车对行人过马路意图的预测能力。该模型整合了四个关键的上下文维度：行人行为、环境、行人定位和车辆运动。通过涉及上下文内和跨上下文注意力机制的渐进式融合策略，MFT旨在实现更准确的预测，在JAADbeh数据集上准确率达到73%，在JAADall数据集上达到93%，在PIE数据集上达到90%，表现优于现有方法。 AI

影响这项研究通过改进对行人行为的预测，有望带来更安全的自动驾驶系统。

排序理由该集群包含一篇详细介绍特定AI应用新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yuanzhe Li, Hang Zhong, Steffen M\"uller · 2026-08-25 04:00

面向城市环境中行人过马路意图预测的多上下文融合Transformer

arXiv:2511.20011v3 Announce Type: replace-cross Abstract: Pedestrian crossing intention prediction is essential for autonomous vehicles to improve pedestrian safety and reduce traffic accidents. However, accurate pedestrian intention prediction in urban environments remains chall…

本文如何被排名

报道来源 [1]

面向城市环境中行人过马路意图预测的多上下文融合Transformer

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