Institute of Computing Technology, Chinese Academy IR
A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing | |
Bi, Huikun1,2,3; Mao, Tianlu2; Wang, Zhaoqi2; Deng, Zhigang4 | |
2020-07-01 | |
发表期刊 | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS |
ISSN | 1077-2626 |
卷号 | 26期号:7页码:2335-2348 |
摘要 | Most of existing traffic simulation methods have been focused on simulating vehicles on freeways or city-scale urban networks. However, relatively little research has been done to simulate intersectional traffic to date despite its broad potential applications. In this paper, we propose a novel deep learning-based framework to simulate and edit intersectional traffic. Specifically, based on an in-house collected intersectional traffic dataset, we employ the combination of convolution network (CNN) and recurrent network (RNN) to learn the patterns of vehicle trajectories in intersectional traffic. Besides simulating novel intersectional traffic, our method can be used to edit existing intersectional traffic. Through many experiments as well as comparative user studies, we demonstrate that the results by our method are visually indistinguishable from ground truth, and our method can outperform existing methods. |
关键词 | Trajectory Solid modeling Computational modeling Vehicle dynamics Traffic control Data models Deep learning Traffic simulation crowd simulation data-driven deep learning intersectional traffic |
DOI | 10.1109/TVCG.2018.2889834 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFC0804900] ; National Natural Science Foundation of China[61532002] ; 13th Five-Year Common Technology pre Research Program[41402050301-170441402065] ; Science and Technology Mobilization Program of Dongguan[KZ2017-06] ; US NSF[IIS 1524782] ; CSC Fellowship |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Software Engineering |
WOS记录号 | WOS:000542933100001 |
出版者 | IEEE COMPUTER SOC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/15133 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Deng, Zhigang |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Houston, Comp Graph & Interact Media Lab, Houston, TX 77204 USA 4.Univ Houston, Comp Sci Dept, Houston, TX 77004 USA |
推荐引用方式 GB/T 7714 | Bi, Huikun,Mao, Tianlu,Wang, Zhaoqi,et al. A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2020,26(7):2335-2348. |
APA | Bi, Huikun,Mao, Tianlu,Wang, Zhaoqi,&Deng, Zhigang.(2020).A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,26(7),2335-2348. |
MLA | Bi, Huikun,et al."A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 26.7(2020):2335-2348. |
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