Browse > Article
http://dx.doi.org/10.12815/kits.2021.20.5.186

Vehicle Localization Method for Lateral Position within Lane Based on Vision and HD Map  

Woo, Rinara (Center for Embedded Software Technology, Univ. of Kyungpook National)
Seo, Dae-Wha (School of Electronics Engineering, Univ. of Kyungpook National)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.5, 2021 , pp. 186-201 More about this Journal
Abstract
As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.
Keywords
Localization; HD map; Lane detection;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Aly M.(2008), "Real time detection of lane markers in urban streets," 2008 IEEE Intelligent Vehicles Symposium.
2 Bernstein D. and Kornhauser A.(1998), An introduction to map matching forpersonal navigation assistants, New Jersey TIDE Center, pp.1-14.
3 Jo K., Jo Y., Suhr J. K., Jung H. G. and Sunwoo M.(2015), "Precise localization of an autonomous car based on probabilistic noise models of road surface marker features using multiple cameras," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp.3377-3392.   DOI
4 Kang J. M., Kim H. S., Park J. B. and Choi Y. H.(2018), "An enhanced map-matching algorithm for real-time position accuracy improvement with a low-cost GPS receiver," Sensors, vol. 18, no. 11, p.3836.   DOI
5 Kim D., Kim B., Chung T. and Yi K.(2017), "Lane-level localization using an avm camera for an automated driving vehicle in urban environments," IEEE/ASME Transactions on Mechatronics, vol. 22, no. 1, pp.280-290.   DOI
6 Mattern N., Schubert R. and Wanielik G.(2010), "High-accurate vehicle localization using digital maps and coherency images," In 2010 IEEE Intelligent Vehicles Symposium, pp.462-469.
7 Ochieng W., Quddus M. and Noland R.(2003), "Map-matching in complex urban road networks," Revista Brasileira de Cartografia, vol. 55, no. 2.
8 Quddus M. A., Noland R. B. and Ochieng W. Y.(2006), "A high accuracy fuzzy logic based map matching algorithm for road transport," Journal of Intelligent Transportation Systems, vol. 10, no. 3, pp.103-115.   DOI
9 Cai H., Hu Z., Huang G., Zhu D. and Su X.(2018), "Integration of gps, monocular vision and high definition(hd) map for accurate vehicle localization," Sensors, vol. 18, no. 10.   DOI
10 Parra I., Sotelo M. A., Llorca D. F. and Ocana M.(2010), "Robust visual odometry for vehicle localization in urban environments," Robotica, vol. 28, no. 3, pp.441-452.   DOI
11 Valiente D., Gil A., Paya L., Sebastian J. M. and Reinoso O.(2017), "Robust visual localization with dynamic uncertainty management in omnidirectional slam," Appl. Sci., vol. 7, p.1294.   DOI
12 Woo R., Yang E. J. and Seo D. W.(2019), "A fuzzy-innovation-based adaptive kalman filter for enhanced vehicle positioning in dense urban environments," Sensors, vol. 19, no. 5, p.1142.   DOI
13 Xing Y., Lv C. and Cao D.(2020), Advanced driver intention inference: Theory and design, Elsevier Science.
14 Tanaka S., Yamada K., Ito T. and Ohkawa T.(2011), "Vehicle detection based on perspective transformation using rear-view camera," International Journal of Vehicular Technology, vol. 2011, pp.1-9.
15 Kim S., Woo R., Yang E. and Seo D.(2019), "Real time multi-lane detection using relevant lines based on line labeling method," In 2019 4th International Conference on Intelligent Transportation Engineering (ICITE), pp.301-305.
16 Deng L., Yang M., Hu B., Li T., Li H. and Wang C.(2019), "Semantic segmentation-based lane-level localization using around view monitoring system," IEEE Sensors Journal, vol. 19, no. 21, pp.10077-10086.   DOI
17 Forsyth D. and Ponce J.(2003), Computer vision: A modern approach, Prentice Hall.
18 Zhou Q., Zhang H., Li Y. and Li Z.(2015), "An adaptive low-cost GNSS/MEMS-IMU tightly-coupled integration system with aiding measurement in agnss signal-challenged environment," Sensors, vol. 15, pp.23953-23982.   DOI
19 Yang G., Chen Z., Li Y. and Su Z.(2019), "Rapid relocation method for mobile robot based on improved orb-slam2 algorithm," Remote Sens, vol. 11, p.149.   DOI
20 Zhang F., Rui T., Yang C. and Shi J.(2019), "Lap-slam: A line-assisted point-based monocular vslam," Electronics, vol. 8, p.243.   DOI
21 Badino H., Huber D. and Kanade T.(2011), "Visual topometric localization," In 2011 IEEE Intelligent Vehicles Symposium (IV), pp.794-799.
22 Cui D., Xue J. and Zheng N.(2016), "Real-time global localization of robotic cars in lane level via lane marking detection and shape registration," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 4, pp.1039-1050.   DOI
23 Hashemiand M. and Karimi H. A.(2014), "A critical review of real-time map-matching algorithms: Current issues and future directions," Computers, Environment and Urban Systems, vol. 48, pp.153-165.   DOI
24 Du X. and Tan K. K.(2016), "Comprehensive and practical vision system for self-driving vehicle lane-level localization," IEEE Transactions on Image Processing, vol. 25, no. 5, pp.2075-2088.   DOI
25 Durrant-Whyte H. and Bailey T.(2006), "Simultaneous localization and mapping: Part i," IEEE Robotics Automation Magazine, vol. 13, no. 2, pp.99-110.   DOI
26 Grompone von Gioi R., Jakubowicz J., Morel J. and Randall G.(2010), "Lsd: A fast line segment detector with a false detection control," IEEE Trans-actions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp.722-732.   DOI
27 Jeong M., Yoon T., Kim E. and Park J.(2020), "Lane-level map-matching method for vehicle localization using gps and camera on a high-definition map," Sensors, vol. 20, no. 8.
28 Hsueh Y. L. and Chen H. C.(2018), "Map matching for low-sampling-rategps trajectories by exploring real-time moving directions," Information Sciences, vol. 433-434, pp.55-69.   DOI