Browse > Article
http://dx.doi.org/10.7746/jkros.2022.17.3.245

Build a Multi-Sensor Dataset for Autonomous Driving in Adverse Weather Conditions  

Sim, Sungdae (ADD)
Min, Jihong (ADD)
Ahn, Seongyong (ADD)
Lee, Jongwoo (ADD)
Lee, Jung Suk (ADD)
Bae, Gwangtak (ADD)
Kim, Byungjun (ADD)
Seo, Junwon (ADD)
Choe, Tok Son (ADD)
Publication Information
The Journal of Korea Robotics Society / v.17, no.3, 2022 , pp. 245-254 More about this Journal
Abstract
Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.
Keywords
Multi-Sensor; Dataset; Autonomous Driving; Calibration; Adverse Weather;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Geiger, P. Lenz, and R. Urtasun, "Are we ready for autonomous driving? the KITTI vision benchmark suite," 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, DOI: 10.1109/CVPR.2012.6248074.   DOI
2 Waymo, Waymo Open Dataset: An autonomous driving dataset, [Online], https://waymo.com/open, Accessed: August 29, 2019.
3 Y. Choi, N. Kim, S. Hwang, K. Park, J. Yoon, K. An, and I. S. Kweon, "KAIST multi-spectral day/night data set for autonomous and assisted driving," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, Mar., 2018, DOI: 10.1109/TITS.2018.2791533.   DOI
4 MATLAB camera calibration toolbox, [Online] https://www.mathworks.com/help/vision/camera-calibration.html, Accessed: April 10, 2018.
5 M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, "The Cityscapes dataset for semantic urban scene understanding." 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016, DOI: 10.1109/CVPR.2016.350.   DOI
6 H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, and O. Beijbom, "nuscenes: A multimodal dataset for autonomous driving," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020, DOI: 10.1109/CVPR42600.2020.01164.   DOI
7 L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, "Fast LIDAR-based road detection using fully convolutional neural networks," 2017 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, USA, 2017, DOI: 10.1109/IVS.2017.7995848.   DOI
8 S. Sim, J. Sock, and K. Kwak, "Indirect correspondence-based robust extrinsic calibration of LiDAR and camera," Sensors, vol. 16, no. 6, Jun., 2016, DOI: 10.3390/s16060933.   DOI
9 R. Joseph and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint rXiv:1804.02767, 2018, DOI: 10.48550/arXiv.1804.02767.   DOI
10 Y. Tianwei, X. Zhou, and P. Krahenbuhl, "Center-based 3d object detection and tracking," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021, DOI: 10.1109/CVPR46437.2021.01161.   DOI
11 C. Zhe, J. Zhang, and D. Tao, "Progressive lidar adaptation for road detection," IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 3, May, 2019, DOI: 10.1109/JAS.2019.1911459.   DOI
12 M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, "Argoverse: 3d tracking and forecasting with rich maps," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long beach, USA, 2019, DOI: 10.1109/CVPR.2019.00895.   DOI
13 Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, Nov., 2000, DOI: 10.1109/34.888718.   DOI
14 Automatic Weather System, Korea Meteorological Agency, [Online], http://www.weather.go.kr/weather/observation/aws_table_popup.jsp, Accessed: Jul. 28, 2020.