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原文文献 Social BiGAT : Kosaraju V, Sadeghian A, Martín Martín R, et al. Social BiGAT: Multimodal Trajectory Forecasting using Bicycle GAN and Graph Atten 阅读全文
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文献引用 Amirian J, Hayet J B, Pettre J. Social Ways: Learning Multi Modal Distributions of Pedestrian Trajectories with GANs[J]. 2019. 文章是继Social LSTM、So 阅读全文
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本文的出发点是一篇期刊论文,但集中探讨的是这篇文章中不确定度估计的原理与过程,行文将与之前的文献报告不同。 原文 Bhattacharyya A , Fritz M , Schiele B . Long-Term On-Board Prediction of People in Traffic Sc 阅读全文
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Citation Al Molegi A , Martínez Ballesté, Antoni, Jabreel M . Move, Attend and Predict: An Attention based Neural Model for People’s Movement Predicti 阅读全文
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文献 Sun L , Yan Z , Mellado S M , et al. 3DOF Pedestrian Trajectory Prediction Learned from Long Term Autonomous Mobile Robot Deployment Data[J]. 2017. 阅读全文
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paper:Gupta A , Johnson J , Fei-Fei L , et al. Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks[J]. 2018. code:https: 阅读全文
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概览 简述 SS LSTM全称Social Scene LSTM,是一种分层的LSTM模型,在已有的考虑相邻路人之间影响的Social LSTM模型之上额外增加考虑了行人背景的因素。SS LSTM架构类似Seq2Seq,由3个Encoder生成的向量拼接后形成1个Decoder的输入,并最终做出轨迹 阅读全文