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Virtual Road Condition Prediction Through License Plates in 3D Simulation

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Computational Collective Intelligence (ICCCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9875))

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Abstract

Predicting the road conditions lie curves, slopes, hills, helps drivers react faster to avoid possible collisions in hypovigilance and besides, this kind of driver assistance system is more crucial for intelligent vehicles. Even though there are many radar, wifi, infrared systems and devices, what we propose in this paper is a monocular license plate segmentation to foresee the road ahead while cruising behind a blinding vehicle. License plates in the precalibrated images from 3D simulation are segmented and analyzed to identify the front car’s angle of repose. Therefore the angles of the road are estimated frame by frame with calculated distances for prediction of the virtual road.

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Acknowledgment

This work and the contribution were also supported by project “Smart Solutions for Ubiquitous Computing Environments” FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2016-2102).

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Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 50003, Hradec Kralove, Czech Republic

    Orcan Alpar & Ondrej Krejcar

Authors
  1. Orcan Alpar
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  2. Ondrej Krejcar
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Corresponding author

Correspondence to Ondrej Krejcar .

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Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Management, Democritus University of Thrace, Orestiada, Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

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Alpar, O., Krejcar, O. (2016). Virtual Road Condition Prediction Through License Plates in 3D Simulation. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_25

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  • DOI: https://doi.org/10.1007/978-3-319-45243-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45242-5

  • Online ISBN: 978-3-319-45243-2

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