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Vision-Based Multi-Future Trajectory Prediction: A Survey

delete2025-08-01
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PRE
AI
R
Renhao Huang
H
Hao Xue
M
Maurice Pagnucco
F
Flora D. Salim
杨嵩 封面图
杨嵩 (Yang Song)
DOI：10.1109/TNNLS.2025.3550350delete
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摘要

摘要

En 中文
基于视觉的轨迹预测是支持自主系统安全与智能行为的一项重要任务。多年来，许多先进方法被提出，实现了空间与时间特征提取的改进。然而，人类行为本质上具有多样性和不确定性。在给定过去轨迹及周边环境信息的情况下，一个智能体未来可能存在多条合理的轨迹。为解决此问题，一项被称为多未来轨迹预测（MTP）的基本任务近年来受到研究关注。该任务旨在为每个智能体生成多样、可接受且可解释的未来预测分布。本文首次对MTP进行了综述，提出了独特的分类体系，并全面分析了相关框架、数据集及评估指标。我们还比较了现有MTP数据集上的模型，并在ForkingPath数据集上开展了实验。最后，我们探讨了多个未来研究方向，以帮助研究人员开发新颖的MTP系统以及其他与MTP类似的多样化学习任务。
Keyword:
Computer vision
deep learning
generative model
human behavior modeling
trajectory prediction

期刊

IEEE Transactions on Neural Networks and Learning Systems 封面图
IEEE Transactions on Neural Networks and Learning Systems
IF:
8.9
论文数:
7.6K
被引数:
7.2W

机构

U
University of New South Wales
学者数:
2.5K
论文数: 1.3K
被引数: 0
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