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
N1 - Publisher Copyright:
© 2019 IEEE.
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.
UR - https://www.scopus.com/pages/publications/85072280716
U2 - 10.1109/IVS.2019.8813836
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 -