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Automatically Building Linking Relations between Lane-Level Map and Commercial Navigation Map Using Topological Networks Matching

Published online by Cambridge University Press:  20 May 2020

Lu Tao ,
Pan Zhang ,
Lixin Yan  and
Dunyao Zhu
Lu Tao*
Affiliation:
(Graduate School of Informatics, Nagoya University, Japan)
Pan Zhang
Affiliation:
(School of Geodesy and Geomatics, Wuhan University, China) (Wuhan KOTEI Informatics Co., Ltd, China)
Lixin Yan
Affiliation:
(School of Transportation and Logistics, East China Jiaotong University, China)
Dunyao Zhu
Affiliation:
(Wuhan KOTEI Informatics Co., Ltd, China) (GNSS Research Centre, Wuhan University, China)
*
(E-mail: [email protected])
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Abstract

The lane-level map, which contains the lane-level information severely lacking in widely used commercial navigation maps, has become an essential data source for autonomous driving systems. The linking relations between lane-level map and commercial navigation map can facilitate an autonomous driving system mapping information between different applications using different maps. In this paper, an approach is proposed to build the linking relations automatically. The different topology networks are first reconstructed into similar structures. Then, to build the linking relations automatically, the adaptive multi-filter algorithm and forward path exploring algorithm are proposed to detect corresponding junctions and paths, respectively. The approach is validated by two real data sets of more than 150 km of roads, mainly highway. The linking relations for nearly 94% of the total road length have been built successfully.

Keywords

Lane-Level MapCommercial Navigation MapTopological Network MatchingLinking Relations
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 5 , September 2020 , pp. 1159 - 1178
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Copyright
Copyright © The Royal Institute of Navigation 2020

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References

REFERENCES

Abdolmajidi, E., Mansourian, A., Will, J. and Harrie, L. (2015). Matching authority and VGI road networks using an extended node-based matching algorithm. Geo-Spatial Information Science, 18, 65–80. https://doi.org/10.1080/10095020.2015.1071065.CrossRefGoogle Scholar
Alonso, J., Milanés, V., Onieva, E., Pérez, J., González, C. and de Pedro, T. (2011). Cartography for cooperative manoeuvres with autonomous land vehicles. The Journal of Navigation, 64, 141–155. https://doi.org/10.1017/S0373463310000275.CrossRefGoogle Scholar
Bender, P., Ziegler, J. and Stiller, C. (2014). Lanelets: Efficient Map Representation for Autonomous Driving. In: Intelligent Vehicles Symposium Proceedings, 2014 IEEE. IEEE, pp. 420–425.Google Scholar
DARPA Urban Challenge. (2007). Route network definition file (RNDF) and mission data file (MDF) formats. Tech. Rep., Defense Advanced Research Projects Agency.Google Scholar
Devogele, T. (2002). A new merging process for data integration based on the discrete Fréchet distance. In: Advances in Spatial Data Handling. Springer, pp. 167–181.CrossRefGoogle Scholar
Doytsher, Y., Filin, S. and Ezra, E. (2001). Transformation of datasets in a linear-based map conflation framework. Surveying and Land Information Systems, 61, 159–169.Google Scholar
Dupuis, M. (2010). Opendrive format specification. VIRES Simulationstechnologie GmbH.Google Scholar
Fan, H., Zhu, F., Liu, C., Zhang, L., Zhuang, L., Li, D., Zhu, W., Hu, J., Li, H. and Kong, Q. (2018). Baidu Apollo EM Motion Planner. ArXiv Prepr. ArXiv180708048.Google Scholar
Goodchild, M. F. (2000). GIS and transportation: status and challenges. GeoInformatica, 4, 127–139. http://dx.doi.org/10.1023/A:1009867905167.CrossRefGoogle Scholar
Guizzo, E. (2011). How Google's self-driving car works. IEEE Spectrum Online, 18, 1132–1141.Google Scholar
Kato, S., Takeuchi, E., Ishiguro, Y., Ninomiya, Y., Takeda, K. and Hamada, T. (2015). An open approach to autonomous vehicles. IEEE Micro, 35, 60–68. https://doi.org/10.1109/MM.2015.133.CrossRefGoogle Scholar
Koukoletsos, T., Haklay, M. and Ellul, C. (2012). Assessing data completeness of VGI through an automated matching procedure for linear data. Transactions in GIS, 16, 477–498. https://doi.org/10.1111/j.1467-9671.2012.01304.x.CrossRefGoogle Scholar
Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., Koch, O., Kuwata, Y., Moore, D., Olson, E., Peters, S., Teo, J., Truax, R., Walter, M., Barrett, D., Epstein, A., Maheloni, K., Moyer, K., Jones, T., Buckley, R., Antone, M., Galejs, R., Krishnamurthy, S. and Williams, J. (2008). A perception-driven autonomous urban vehicle. Journal of Field Robotics, 25, 727–774. https://doi.org/10.1002/rob.20262.CrossRefGoogle Scholar
Liu, L., Wu, T., Fang, Y., Hu, T. and Song, J. (2015). A Smart Map Representation for Autonomous Vehicle Navigation. In: Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference On. IEEE, pp. 2308–2313.Google Scholar
Mustière, S. and Devogele, T. (2008). Matching networks with different levels of detail. GeoInformatica, 12, 435–453. https://doi.org/10.1007/s10707-007-0040-1.CrossRefGoogle Scholar
Naranjo, J. E., Jiménez, F., Aparicio, F. and Zato, J. (2009). GPS and inertial systems for high precision positioning on motorways. The Journal of Navigation, 62, 351. https://doi.org/10.1017/S0373463308005249.CrossRefGoogle Scholar
Nedevschi, S., Popescu, V., Danescu, R., Marita, T. and Oniga, F. (2013). Accurate ego-vehicle global localization at intersections through alignment of visual data with digital map. IEEE Transactions on Intelligent Transportation Systems, 14, 673–687. https://doi.org/10.1109/TITS.2012.2228191.CrossRefGoogle Scholar
Nyerges, T. L. (1990). Locational referencing and highway segmentation in a geographic information system. ITE Journal, 60, 27–31.Google Scholar
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D. and Ang, M. (2017). Perception, planning, control, and coordination for autonomous vehicles. Machines, 5, 6. https://doi.org/10.3390/machines5010006.CrossRefGoogle Scholar
Saalfeld, A. (1988). Conflation automated map compilation. International Journal of Geographical Information Systems, 2, 217–228. https://doi.org/10.1080/02693798808927897.CrossRefGoogle Scholar
Safra, E., Kanza, Y., Sagiv, Y. and Doytsher, Y. (2006). Efficient Integration of Road Maps, In: Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems. ACM, pp. 59–66.Google Scholar
Tao, Z., Bonnifait, P., Fremont, V. and Ibanez-Guzman, J. (2013). Mapping and Localization Using GPS, Lane Markings and Proprioceptive Sensors, In: Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference On. IEEE, pp. 406–412.Google Scholar
Uitermark, H. T. and Cadastre, D. (1996). The Integration of Geographic Databases: Realising Geodata Interoperability Through the Hypermap Metaphor and a Mediator Architecture, In: Proceedings of the Second Joint European Conference & Exhibition on Geographical Information (Vol. 1): From Research to Application through Cooperation. IOS Press, pp. 92–95.Google Scholar
Volz, S. (2006). An iterative approach for matching multiple representations of street data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36, 101–110.Google Scholar
Volz, S. and Walter, V. (2004). Linking Different Geospatial Databases by Explicit Relations, In: Proceedings of the XXth International Society for Photogrammetry and Remote Sensing (ISPRS) Congress, Comm. IV. pp. 152–157.Google Scholar
Zhang, M., Shi, W. and Meng, L. (2005). A Generic Matching Algorithm for Line Networks of Different Resolutions. In: Workshop of ICA Commission on Generalization and Multiple Representation. Computing Faculty of Coruña University-Campus de Elviña, Spain. Citeseer.Google Scholar
Zhang, M., Yao, W. and Meng, L. (2016). Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation. ISPRS International Journal of Geo-Information, 5, 68. https://doi.org/10.3390/ijgi5050068.CrossRefGoogle Scholar
Ziegler, J., Bender, P., Schreiber, M., Lategahn, H., Strauss, T., Stiller, C., Dang, T., Franke, U., Appenrodt, N. and Keller, C. G. (2014). Making Bertha drive—An autonomous journey on a historic route. IEEE Intelligent Transportation Systems Magazine, 6, 8–20.CrossRefGoogle Scholar