[1] Xia Liang, Li Xu, Li Honghai,. Efficient and reliable road modelingfor digital maps based on cardinal spline [J]. Journal of Southeast University (English Edition), 2018, 34 (1): 48-53. [doi:10.3969/j.issn.1003-7985.2018.01.008]

Copy

Efficient and reliable road modelingfor digital maps based on cardinal spline()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

34

Issue:

2018 1

Page:

48-53

Research Field:

Traffic and Transportation Engineering

Publishing date:

2018-03-20

Info

Title:

Efficient and reliable road modelingfor digital maps based on cardinal spline

Author(s):

Xia Liang¹;  Li Xu¹;  Li Honghai²

¹School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
²Key Laboratory of Technology on Intelligent Transportation Systems, Research Institute of Highway of Ministry of Transport, Beijing 100088, China

Keywords:

cardinal spline;  digital map;  road modeling;  gradual optimization;  optimal balance

PACS:

U411

DOI:

10.3969/j.issn.1003-7985.2018.01.008

Abstract:

In order to realize an optimal balance between the efficiency and reliability requirements of road models, a road modeling method for digital maps based on cardinal spline is studied. First, the cardinal spline is chosen to establish an initial road model, which is specified by a series of control points and tension parameters. Then, in view of the initial road model, a gradual optimization algorithm, which can determine the reasonable control points and optimal tension parameters according to the degree of the change of road curvature, is proposed to determine the final road model. Finally, the proposed road modeling method is verified and evaluated through experiments, and it is compared with the conventional method for digital maps based on the B-spline. The results show that the proposed method can realize a near-optimal balance between the efficiency and reliability requirements. Compared with the conventional method based on the B-spline, this method occupies less data storage and achieves higher accuracy.

References:

[1] Jo K, Sunwoo M. Generation of a precise roadway map for autonomous cars[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(3): 925-937. DOI:10.1109/tits.2013.2291395.
[2] Kim S W, Liu W, Ang M H, et al. The impact of cooperative perception on decision making and planning of autonomous vehicles [J]. IEEE Intelligent Transportation Systems Magazine, 2015, 7(3): 39-50. DOI:10.1109/mits.2015.2409883.
[3] Gwon G P, Hur W S, Kim S W, et al. Generation of a precise and efficient lane-level road map for intelligent vehicle systems [J]. IEEE Transactions on Vehicular Technology, 2017, 66(6): 4517-4533. DOI:10.1109/tvt.2016.2535210.
[4] 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): 2355-2366. DOI:10.1109/tits.2016.2521819.
[5] Du J, Barth M J. Next-generation automated vehicle location systems: Positioning at the lane level [J]. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(1): 48-57.
[6] Ziegler J, Bender P, Schreiber M, et al. Making Bertha drive—An autonomous journey on a historic route [J].IEEE Intelligent Transportation Systems Magazine, 2014, 6(2): 8-20.
[7] Jiménez F, Naranjo J E, García F, et al. Limitations of positioning systems for developing digital maps and locating vehicles according to the specifications of future driver assistance systems [J]. IET Intelligent Transport Systems, 2011, 5(1): 60-69. DOI:10.1049/iet-its.2010.0042.
[8] Okaniwa S, Nasri A, Lin H, et al. Uniform B-spline curve interpolation with prescribed tangent and curvature vectors [J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(9): 1474-1487.
[9] Ben-Arieh D, Chang S, Rys M, et al. Geometric modeling of highways using global positioning system data and B-spline approximation [J]. Journal of Transportation Engineering, 2004, 130(5): 632-636. DOI:10.1061/(asce)0733-947x(2004)130:5(632).
[10] Wedel A, Badino H, Rabe C, et al. B-spline modeling of road surfaces with an application to free-space estimation [J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(4): 572-583. DOI:10.1109/tits.2009.2027223.
[11] Zhang T, Arrigoni S, Garozzo M, et al. A lane-level road network model with global continuity [J]. Transportation Research Part C: Emerging Technologies, 2016, 71(1): 32-50. DOI:10.1016/j.trc.2016.07.003.
[12] Bhandari A, Marziliano P. Fractional delay filters based on generalized cardinal exponential splines [J]. IEEE Signal Processing Letters, 2010, 17(3): 225-228. DOI:10.1109/lsp.2009.2036386.
[13] Wang S, Qin S, Guan C. Feature-based human model for digital apparel design [J]. IEEE Transactions on Automation Science and Engineering, 2014, 11(2): 620-626. DOI:10.1109/tase.2014.2300876.
[14] NovAtel Inc. SPAN-CPT single enclosure GNSS/INS receiver[EB/OL].(2017-01-05)[2017-08-31]. http://www.novatel.com/support/info/documents/564.

Memo

Memo:

Biographies: Xia Liang(1992—), male, graduate; Li Xu(corresponding author), male, doctor, professor, lixu.mail@163.com.
Foundation items: The National Natural Science Foundation of China(No. 61273236), the National Key Research and Development Plan of China(No.2016YFC0802706, 2017YFC0804804), the Program for Special Talents in Six Major Fields of Jiangsu Province(No.2017JXQC-003), the Project of Beijing Municipal Science and Technology Commission(No.Z161100001416001).
Citation: Xia Liang, Li Xu, Li Honghai.Efficient and reliable road modeling for digital maps based on cardinal spline[J].Journal of Southeast University(English Edition), 2018, 34(1):48-53.DOI:10.3969/j.issn.1003-7985.2018.01.008.

Common functions