2018, Volume 20, Issue 2
Strategic Study of CAE >> 2018, Volume 20, Issue 2 doi: 10.15302/J-SSCAE-2018.02.015
Progress and Consideration of High Precision Road Navigation Map
1. Wuhan University, Wuhan 430079, China;
2. Tongji University, Shanghai 200092, China;
3. EMG Technologies (Beijing) Co., Ltd., Beijing 100070, China
Next Previous
Abstract
With the rapid development of the Internet, an increasing number of new industries such as "Internet Plus" intelligent transportation and unmanned systems based on location-based services have been gradually developed. The development of these industries requires the support of high precision location data, which the 5 m accuracy of traditional navigation maps cannot provide. To overcome the drawbacks of traditional maps, high precision road navigation maps have been proposed. High precision road navigation maps can provide more detailed road information and are thus able to more accurately reflect the real situation of roads. Compared to traditional maps, high precision road navigation maps possess three advantages. First, they include additional map layers. Second, the content of the layers is finer. Third, a new map structure is divided. However, the rich information content of high precision maps leads to the generation of huge amounts of data. Traditional centralized big data processing modes are unable to meet the computing needs required for processing such huge amounts of data. Therefore, in this paper, we propose a big data processing model involving "crowdsourcing + edge computing" to address the problem of high precision map calculation. At present, high precision road navigation maps have kicked into a high gear. Nevertheless, certain problems persist that need to be addressed during the process of development.
Keywords
high precision road navigation map ; “Internet Plus” intelligent transportation ; unmanned systems ; crowdsourcing ; edge computing
Figures
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
References
[ 1 ] Liu S S, Tang J. The first book to introduce self-driving technolo-gy [M]. Beijing: Publishing House of Electronics Industry, 2017.
[ 2 ] Tao Z, Bonnifait P, Fremont V, et al. Mapping and localization using GPS, lane markings and proprioceptive sensors [C]. Tokyo: Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ Interna-tional Conference on IEEE, 2013. link1 link2
[ 3 ] Nedevschi S, Popescu V, Danescu R, et al. Accurate ego-vehicle global localization at intersections through alignment of visual data with digital map [J]. Intelligent Transportation Systems, 2013, 14(2): 673–687. link1 link2
[ 4 ] He Y. GPS navigation methods research based on high precision lane-level map [D]. Shanghai: Shanghai Jiao Tong Universi-ty(Master’s thesis), 2015. link1 link2
[ 5 ] Suganuma J, Uozumi T. Precise position estimation of autono-Standard system framework of high-precision electronic mapBasic standard of mapStandard of data collectionStandard of mappingStandard of data storageStandard of calculation modeStandard of cloudintegrationStandard ofapplication serviceStandard of testand evaluationImage recognition schemeStandard for classification ofmap elementsStandard of term definitionStandard of cloud storageStandard of end storagStandard of computing methodStandard of outputLaser radar scheme Unmanned drivingRobotFunctionSecurityEnvironmentUsabilityEmbedded location-based serviceFig. 8. Standard system framework of high-precision electronic map.mous vehicle based on map-matching [J]. IEEE Intelligent Vehicle Symposium, 2011 (4): 296–301. link1 link2
[ 6 ] Levinson J, Montemerlo M, Thrun S. Map-based precision vehicle localization in urban environments [M]. Cambridge: MIT Press, 2007. link1 link2
[ 7 ] Hao L, Nashashibi F, Toulminet G. Localization for intelligent vehicle by fusing mono camera low-cost GPS and map data [J]. Intelligent Transportation Systems, 2010 (9): 1657-1662. link1 link2
[ 8 ] Ress C, Etemad A, Kuck D, et al. Electronic horizon—Providing digital map data for ADAS applications [J]. Madeira, 2008 (3): 40–49.
[ 9 ] Sutarwala B Z. GIS for mapping of lane-level data and re-creation in real time for navigation [D]. Riverside: University of California (Master’s thesis), 2010. link1 link2
[10] Schreiber M, Knoppel C, Franke U. Laneloc: Lane marking based localization using highly accurate maps [J]. IEEE Xplore, 2013, 36(1): 449–454. link1 link2
[11] Guo H Z, Meguro J I, Kojima Y, et al. Automatic lane-level map generation for autonomous robotic cars and advanced driver as-sistance systems using low-cost sensors [C]. Hong Kong: IEEE International Conference on Robotics & Automation ICRA, 2014.
[12] Bender P, Ziegler J, Stiller C. Lanelets: Efficient map representa-tion for autonomous driving [J]. Intelligent Vehicles Symposi-um, 2014 (3): 420–425. link1 link2
[13] Peng X, Wang M Y, Zeng J R, et al. Application of high precision BDS positioning technology for traffic management law enforce-ment and evidence collection[J].Application of Electronic Tech-nique, 2017, 43(4): 21–23. link1 link2
[14] Shi W S, Cao J, Zhang Q, et al. Edge computing: Vision and chal-lenges [J]. IEEE Internet of Things Journal, 2016, 3(5): 637–646. link1 link2
[15] Shi W S, Song H, Cao J, et al. Edge computing: An emerging computing model for the Internet of everything era [J]. Journal of Computer Research and Development, 2017, 54(5): 907–924. link1 link2
京公网安备 11010502051620号
