Road scene layout reconstruction based on cnn and its application in traffic simulation

Chao Zhu; Yaochen Li; Yuehu Liu; Zhiqiang Tian; Zhichao Cui; Chi Zhang; Xinyu Zhu

doi:10.1109/IVS.2019.8813836

Road scene layout reconstruction based on cnn and its application in traffic simulation

Chao Zhu
, Yaochen Li
, Yuehu Liu
, Zhiqiang Tian
, Zhichao Cui
, Chi Zhang
, Xinyu Zhu

College of Artificial Intelligence and Robotics

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In this paper, we propose a road scene prediction framework based on the control points of road boundaries using CNN. Firstly, the image features are extracted and the heatmaps are generated by CNN to locate the control points of road boundaries. The input images are then segmented to specify the scene layout based on the control points. Furthermore, the 3D traffic scene models are constructed. The applications for traffic simulation are then developed. The evaluations and comparisons based on TSD-max dataset prove the effectiveness of the proposed method.

Original language	English
Title of host publication	2019 IEEE Intelligent Vehicles Symposium, IV 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	480-485
Number of pages	6
ISBN (Electronic)	9781728105604
DOIs	https://doi.org/10.1109/IVS.2019.8813836
State	Published - Jun 2019
Event	30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, France Duration: 9 Jun 2019 → 12 Jun 2019

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings
Volume	2019-June

Conference

Conference	30th IEEE Intelligent Vehicles Symposium, IV 2019
Country/Territory	France
City	Paris
Period	9/06/19 → 12/06/19

Access to Document

10.1109/IVS.2019.8813836

Cite this

Zhu, C., Li, Y., Liu, Y., Tian, Z., Cui, Z., Zhang, C., & Zhu, X. (2019). Road scene layout reconstruction based on cnn and its application in traffic simulation. In 2019 IEEE Intelligent Vehicles Symposium, IV 2019 (pp. 480-485). Article 8813836 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2019.8813836

@inproceedings{169d56ecd6044d028a2e0764df67e9d3,

title = "Road scene layout reconstruction based on cnn and its application in traffic simulation",

abstract = "In this paper, we propose a road scene prediction framework based on the control points of road boundaries using CNN. Firstly, the image features are extracted and the heatmaps are generated by CNN to locate the control points of road boundaries. The input images are then segmented to specify the scene layout based on the control points. Furthermore, the 3D traffic scene models are constructed. The applications for traffic simulation are then developed. The evaluations and comparisons based on TSD-max dataset prove the effectiveness of the proposed method.",

author = "Chao Zhu and Yaochen Li and Yuehu Liu and Zhiqiang Tian and Zhichao Cui and Chi Zhang and Xinyu Zhu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 30th IEEE Intelligent Vehicles Symposium, IV 2019 ; Conference date: 09-06-2019 Through 12-06-2019",

year = "2019",

month = jun,

doi = "10.1109/IVS.2019.8813836",

language = "英语",

series = "IEEE Intelligent Vehicles Symposium, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "480--485",

booktitle = "2019 IEEE Intelligent Vehicles Symposium, IV 2019",

}

Zhu, C, Li, Y, Liu, Y, Tian, Z, Cui, Z, Zhang, C & Zhu, X 2019, Road scene layout reconstruction based on cnn and its application in traffic simulation. in 2019 IEEE Intelligent Vehicles Symposium, IV 2019., 8813836, IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. 480-485, 30th IEEE Intelligent Vehicles Symposium, IV 2019, Paris, France, 9/06/19. https://doi.org/10.1109/IVS.2019.8813836

Road scene layout reconstruction based on cnn and its application in traffic simulation. / Zhu, Chao; Li, Yaochen; Liu, Yuehu et al.
2019 IEEE Intelligent Vehicles Symposium, IV 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 480-485 8813836 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Road scene layout reconstruction based on cnn and its application in traffic simulation

AU - Zhu, Chao

AU - Li, Yaochen

AU - Liu, Yuehu

AU - Tian, Zhiqiang

AU - Cui, Zhichao

AU - Zhang, Chi

AU - Zhu, Xinyu

PY - 2019/6

Y1 - 2019/6

N2 - In this paper, we propose a road scene prediction framework based on the control points of road boundaries using CNN. Firstly, the image features are extracted and the heatmaps are generated by CNN to locate the control points of road boundaries. The input images are then segmented to specify the scene layout based on the control points. Furthermore, the 3D traffic scene models are constructed. The applications for traffic simulation are then developed. The evaluations and comparisons based on TSD-max dataset prove the effectiveness of the proposed method.

AB - In this paper, we propose a road scene prediction framework based on the control points of road boundaries using CNN. Firstly, the image features are extracted and the heatmaps are generated by CNN to locate the control points of road boundaries. The input images are then segmented to specify the scene layout based on the control points. Furthermore, the 3D traffic scene models are constructed. The applications for traffic simulation are then developed. The evaluations and comparisons based on TSD-max dataset prove the effectiveness of the proposed method.

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DO - 10.1109/IVS.2019.8813836

M3 - 会议稿件

AN - SCOPUS:85072280716

T3 - IEEE Intelligent Vehicles Symposium, Proceedings

SP - 480

EP - 485

BT - 2019 IEEE Intelligent Vehicles Symposium, IV 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 30th IEEE Intelligent Vehicles Symposium, IV 2019

Y2 - 9 June 2019 through 12 June 2019

ER -

Road scene layout reconstruction based on cnn and its application in traffic simulation

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