TrajVAE: A Variational AutoEncoder model for trajectory generation
TrajVAE: A Variational AutoEncoder model for trajectory generation
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TrajVAE：用于轨迹生成的变分自动编码器模型
DOI：
10.1016/j.neucom.2020.03.120
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发表时间：
2021-01-15
期刊：
影响因子：
6
通讯作者：
Liu, Chengfei
中科院分区：
文献类型：
--
作者：
Chen, Xinyu;Xu, Jiajie;Liu, Chengfei
自动驾驶和许多其他应用总是需要大规模的轨迹数据集。针对数据稀疏城市或区域中自动驾驶仿真和交通分析任务对大规模轨迹数据的需求，研究了轨迹生成问题，旨在生成与真实的轨迹难以区分的合格轨迹数据集。我们提出了两种先进的解决方案，即TrajGAN和TrajVAE，它们首先利用LSTM对轨迹的特征进行建模，然后分别利用生成对抗网络（GAN）和变分自动编码器（VAE）框架来生成轨迹。为了比较我们的数据集和生成的轨迹中现有轨迹的相似性，我们利用多个轨迹相似性度量。通过多次实验，我们证明我们的方法比基线更准确、更稳定。(C)2020 Elsevier B.V.保留所有权利。
Large-scale trajectory dataset is always required for self-driving and many other applications. In this paper, we focus on the trajectory generation problem, which aims to generate qualified trajectory dataset that is indistinguishable from real trajectories, for fulfilling the needs of large-scale trajectory data by self-driving simulation and traffic analysis tasks in data sparse cities or regions. We propose two advanced solutions, namely TrajGAN and TrajVAE, which utilize LSTM to model the characteristics of trajectories first, and then take advantage of Generative Adversarial Network (GAN) and Variational AutoEncoder (VAE) frameworks respectively to generate trajectories. In order of compare the similarity of existing trajectories in our dataset and the generated trajectories, we utilize multiple trajectory similarity metrics. Through several experiments, we demonstrate that our method is more accurate and stable than the baseline. (C) 2020 Elsevier B.V. All rights reserved.