Laser Data Based Automatic Generation of Lane-Level Road Map for Intelligent Vehicles
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32919
Laser Data Based Automatic Generation of Lane-Level Road Map for Intelligent Vehicles

Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang


With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: Curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 490


[1] Y.-W. Seo, C. Urmson, and D. Wettergreen, “Exploiting publicly available cartographic resources for aerial image analysis,” in Proc.20th ACM SIGSPATIAL Int. Conf. Adv. Geographic Inf. Syst., 2012,pp. 109–118.
[2] M. Amo, F. Martinez, and M. Torre, “Road extraction from aerial images using a region competition algorithm,” IEEE Trans. Image Process., vol. 15, no. 5, pp. 1192–1201, May 2006.
[3] Guo C, Kidono K, Meguro J, et al. A Low-Cost Solution for Automatic Lane-Level Map Generation Using Conventional In-Car Sensors (J). IEEE Transactions on Intelligent Transportation Systems, 2016, 17(8):1-12.
[4] Jaakkola, A., Hyyppä, J., Hyyppä, H., Kukko, A., 2008. Retrieval algorithms for road surface modelling using laser-based mobile mapping. Sensors 8, 5238–5249.
[5] H. Guan et al., “Using mobile laser scanning data for automated extraction of road markings,” ISPRS J. Photogramm. Remote Sens., vol. 87, pp. 93–107, Jan. 2014.
[6] A. Joshi and M. R. James, “Generation of accurate lane-level maps from coarse prior maps and lidar,” IEEE Intell. Transp. Syst. Mag., vol. 7,no. 1, pp. 19–29, Spring 2015
[7] Gi-Poong, Gwon, Woo-Sol, Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems (J). IEEE Transactions on Vehicular Technology, 2017.
[8] Mengmeng, Yang, et al. "Laser data based automatic recognition and maintenance of road markings from MLS system." Optics & Laser Technology 107(2018):192-203.
[9] Cheng, Ming, et al. "Extraction and Classification of Road Markings Using Mobile Laser Scanning Point Clouds." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017.
[10] Jung, Jaehoon , et al. "Efficient and robust lane marking extraction from mobile lidar point clouds." ISPRS Journal of Photogrammetry and Remote Sensing 147.JAN.(2019):1-18..
[11] M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov, S. Ettinger, D. Haehnel, T. Hilden, G. Hoffmann, B. Huhnke, D. Johnston, S. Klumpp, D. Langer, A. Levandowski, J. Levinson, J. Marcil, D. Orenstein, J. Paefgen, I. Penny, A. Petrovskaya, M. Pflueger, G. Stanek, D. Stavens, A. Vogt, and S. Thrun, “Junior: The stanfordentry in the urban challenge,” Journal of Field Robotics, vol. 25, no. 9,pp. 569–597, 2008.
[12] Hata, Alberto Y., F. S. Osorio , and D. F. Wolf . "Robust curb detection and vehicle localization in urban environments." Intelligent Vehicles Symposium IEEE, 2014.
[13] Samples, Michael, and Michael R. James. "Learning a real-time 3D point cloud obstacle discriminator via bootstrapping." Workshop on Robotics and Intelligent Transportation System. 2010.
[14] Chen, Tongtong, et al. "Gaussian-Process-Based Real-Time Ground Segmentation for Autonomous Land Vehicles." Journal of Intelligent and Robotic Systems 76.3-4(2014):563-582.
[15] Bogoslavskyi, Igor, and C. Stachniss. "Efficient Online Segmentation for Sparse 3D Laser Scans." PFG – Journal of Photogrammetry Remote Sensing and Geoinformation ence (2017):41-52.