Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Research on vehicle intelligent wireless location algorithm based on convolutional neural network

  • S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Vehicle positioning and vehicle identification of natural scene images are an important part of intelligent transportation systems and unmanned driving research. In current situation, there are still some problems in vehicle intelligent wireless positioning. In order to improve the intelligent wireless positioning efficiency of vehicles, based on the convolutional neural network, this research combines the concept of deep learning to carry out algorithm innovation in the research. Moreover, this paper combines the actual vehicle positioning problem points to collect data, simulates the vehicle positioning situation in a variety of complex situations, and designs a controlled test to verify. The results show that the algorithm of this study has certain effects, which can provide reference for subsequent related research and has certain practical significance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Netland Ø, Farstad JMG, Welo T (2017) Design of a modular extrusion-based aluminum monorail system for highly accurate car positioning. Procedia CIRP 60:8–13

    Article  Google Scholar 

  2. Zhining G, Wei G, Chaoyang L et al (2018) An adaptive method for switching between pedestrian/car indoor positioning algorithms based on multilayer time sequences. Sensors 18(3):711

    Article  Google Scholar 

  3. Nguyen P, Nguyen H, Nguyen D et al (2017) ParkSense: automatic parking positioning by leveraging in-vehicle magnetic field variation. IEEE Access 99:1

    Google Scholar 

  4. Guo ZG, Mak KL (2004) A heuristic algorithm for the stochastic vehicle routing problems with soft time windows. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753), vol 2. IEEE, pp 1449–1456

  5. Liew LS, Wong WSH (2014) Indoor positioning method based on inertial data, RSSI and compass from handheld smart-device. In: Proceedings of the 2014 international conference on computer, communications, and control technology (I4CT). IEEE, pp 48–52

  6. Shi X, Anderson BDO, Mao G et al (2016) Robust localization using time difference of arrivals. IEEE Signal Process Lett 23(10):1320–1324

    Article  Google Scholar 

  7. Ou CH (2014) A roadside unit-based localization scheme for vehicular ad hoc networks. Int J Commun Syst 27(1):135–150

    Article  Google Scholar 

  8. Hsu HH, Chen CC (2010) RFID-based human behavior modeling and anomaly detection for elderly care. Mob Inf Syst 6(4):341–354

    Google Scholar 

  9. Li L, Yang W, Bhuiyan MZA et al (2016) Unsupervised learning of indoor localization based on received signal strength: unsupervised learning of indoor localization. Wirel Commun Mob Comput 16(15):2225–2237

    Article  Google Scholar 

  10. Bounini F, Gingras D, Lapointe V, et al (2015) Autonomous vehicle and real time road lanes detection and tracking. In: Proceedings of the 2015 IEEE vehicle power and propulsion conference (VPPC). IEEE, pp 1–6

  11. Sakr AH, Bansal G (2016) Cooperative localization via DSRC and multi-scnsor multi-target track association. In: Proceedings of the IEEE, international conference on intelligent transportation systems. IEEE, pp 66–71

  12. JinmingH A (2017) Method of BDS/GPS RTK positioning based on MCAR algorithm for medium-baseline. Geomat Inf Sci Wuhan Univ 42(9):1216–1222

    Google Scholar 

  13. Auer M, Rehborn H, Molzahn SE et al (2017) Traffic services for vehicles: the process from receiving raw probe data to space-time diagrams and the resulting traffic service. Front Eng Manag 4(4):490–497

    Article  Google Scholar 

  14. Alejandro C, Guillem B, Antoni M et al (2017) Autonomous car parking system through a cooperative vehicular positioning network. Sensors 17(4):848

    Article  Google Scholar 

  15. Sheng-Shih W (2018) A BLE-based pedestrian navigation system for car searching in indoor parking garages. Sensors 18(5):1442

    Article  Google Scholar 

  16. Wenwei Z, Chi G, Jingnan L et al (2018) A police and insurance joint management system based on high precision BDS/GPS positioning. Sensors 18(2):169

    Article  Google Scholar 

  17. Li Z, Pei Q, Markwood I et al (2018) Location privacy violation via GPS-agnostic smart phone car tracking. IEEE Trans Veh Technol 67(6):1

    Article  Google Scholar 

Download references

Acknowledgement

This study was supported by National Natural Science Foundation of China (No. 31702232), Education teaching reform project of Henan (No. 2019-JSJYYB-056) and Education teaching reform project of Zhoukou normal university (No. J2019002).

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, ZhouKou Normal University, 466001, Zhoukou, Henan, China

    Yazi Wang

  2. College of Applied Sciences, Beijing University of Technology, 100022, Beijing, China

    Yuehong Feng

  3. School of Mathematics and Statistics, Fuyang Normal University, 466001, Fuyang, Henan, China

    Huaibo Sun

Authors
  1. Yazi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuehong Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huaibo Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huaibo Sun.

Ethics declarations

Conflict of interest

The authors have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Feng, Y. & Sun, H. Research on vehicle intelligent wireless location algorithm based on convolutional neural network. Neural Comput & Applic 33, 8131–8141 (2021). https://doi.org/10.1007/s00521-020-04911-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04911-w

Keywords

Search

Navigation