[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3387168.3387197acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicvispConference Proceedingsconference-collections
research-article

A Novel Method for Extending V2V System

Published: 25 May 2020 Publication History

Abstract

Autonomous vehicles requires sufficient perception of the surrounding environment to make proper driving behavior. Vehicle-to-vehicle (V2V) is a technology that allow vehicles exchange location information (i.e. velocity, position) which can improve the perception capabilities of traditional on-board sensors. However, there are still obstacles preventing the roll-out of the V2X technology, mainly the fact that, unless almost the totality of the existing vehicles adopt it, its effectiveness is rather limited. We can't guarantee that all vehicles are V2V vehicles in real environment due to many reasons. In the traditional V2V system, only V2V vehicle have the ability to broadcast their own location information, but non-V2V vehicle can't. But, the situation is somewhat different in our V2V system. Although, non-V2V vehicles don't have the ability to broadcast their own location information, we can let V2V vehicle detect the location information of non-V2V vehicle and broadcast them out. Therefore, we can think that the non-V2V vehicle can also have the ability to broadcast its own location information in our V2V system. In this way, we extend the ability of traditional V2V system to a certain extent. The proposed method is validated under real-world conditions in urban area.

References

[1]
Blanc, C., Trassoudaine, L., and Gallice, J. (2005). Ekf and particle filter track-to-track fusion: a quantitative comparison from radar/lidar obstacle tracks. In Information Fusion, 2005 8th International Conference on, volume 2, pages 7-pp. IEEE.
[2]
Chang, S. L., Chen, L. S., Chung, Y. C., and Chen, S. W. (2004). Automatic license plate recognition. IEEE Transactions on Intelligent Transportation Systems, 5(1):42--53.
[3]
Dietmayer, K. C., Sparbert, J., and Streller, D. (2001). Model based object classification and object tracking in traffic scenes from range images. In Proceedings of IV IEEE Intelligent Vehicles Symposium, volume 200.
[4]
Du, S., Ibrahim, M., Shehata, M., and Badawy, W. (2013).Automatic license plate recognition (alpr): A stateof-the-art review. IEEE Transactions on Circuits & Systems for Video Technology, 23(2):311--325.
[5]
Garca-Osorio, C., Dez-Pastor, J. F., Rodrguez, J. J., and Maudes, J. (2010). License plate number recognition - new heuristics and a comparative study of classifiers. In Icinco 2008, Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics, Robotics and Automation 1, Funchal, Madeira, Portugal, May, pages 268--273.
[6]
Jida, B. (2008). Integration du contexte par r' eseaux' bayesiens pour la d ' etection et le suivi multi-cibles.' Universite du Littoral C' ote d'Opale.
[7]
Klasing, K., Wollherr, D., and Buss, M. (2008). A clustering method for efficient segmentation of 3d laser data. In IEEE International Conference on Robotics and Automation, pages 4043--4048.
[8]
Lee, K. J. (2001). Reactive navigation for an outdoor autonomous vehicle. Tech. Report.
[9]
Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., and Huhnke, B. (2009). Junior: The Stanford Entry in the Urban Challenge. Springer Berlin Heidelberg.
[10]
Nagare, A. P. (2011). License plate character recognition system using neural network. International Journal of Computer Applications, 25(10):36--39.
[11]
Park, Y., Yun, S., Won, C. S., Cho, K., Um, K., and Sim, S. (2014). Calibration between color camera and 3d lidar instruments with a polygonal planar board. Sensors, 14(3):5333--53.
[12]
Petrovskaya, A. and Thrun, S. (2008). Model based vehicle tracking for autonomous driving in urban environments. Proceedings of robotics: science and systems IV, Zurich, Switzerland, 34.
[13]
Premebida, C. and Nunes, U. (2005). Segmentation and geometric primitives extraction from 2d laser range data for mobile robot applications. Robotica, 2005:17--25.
[14]
Sarfraz, M. S., Shahzad, A., Elahi, M. A., Fraz, M., Zafar, I., and Edirisinghe, E. A. (2013). Real-time automatic license plate recognition for cctv forensic applications. Journal of Real-Time Image Processing, 8(3):285--295.
[15]
Thrun, S. (2010). Toward robotic cars. Communications of the Acm, 53(4):99--106.
[16]
Tielert, T., Rieger, D., Hartenstein, H., and Luz, R. (2012). Can v2x communication help electric vehicles save energy In International Conference on ITS Telecommunications, pages 232--237.

Index Terms

  1. A Novel Method for Extending V2V System

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing
    August 2019
    584 pages
    ISBN:9781450376259
    DOI:10.1145/3387168
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 May 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Extend V2V System
    2. Intelligent Vehicles
    3. License Plate Number Recognition
    4. Non-V2V Vehicle Detection
    5. V2V Communications

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICVISP 2019

    Acceptance Rates

    ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;
    Overall Acceptance Rate 186 of 424 submissions, 44%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 74
      Total Downloads
    • Downloads (Last 12 months)9
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 18 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media