QR Code Based Signage to Support Automated Driving Systems on Rural Area Roads

  • Erol OzanEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 281)


Automated Driving Systems (ADS) have significant potential to improve road safety and efficiency. The performance and the market adoption of ADS depend on the availability of the infrastructure that can support the operation of autonomous or semi-autonomous vehicles throughout the road networks. Rural and remote areas pose significant problems because they often lack power and communication network required for ADS functionality. There is a need for low-cost machine-readable static signage that can fill the gaps in the infrastructure. As one of the early steps to achieve that goal, this paper explores the feasibility of using Quick Response (QR) codes as static road side signage that can inform and alert machine-based onboard systems about the road conditions ahead. It specifically focuses on the applications involving the relay of data pertaining to the high accuracy roadway geometric information of intersections.


Image analysis Surface transport systems Automated driving systems 


  1. 1.
    Denso: QR code essentials. Denso (2011)Google Scholar
  2. 2.
    GSI AISBL: GS1 DataMatrix Guideline. GS1 (2018)Google Scholar
  3. 3.
    California PATH Program: Investigating the Potential Benefits of Broadcasted Signal Phase and Timing (SPaT) Data under IntelliDrive. Institute of Transportation Studies, University of California, Berkeley (2011)Google Scholar
  4. 4. California CV Testbed. Accessed 28 Feb 2018
  5. 5.
    Grillo, A., Lentini, A., Querini, M., Italiano, G.: High capacity colored two dimensional codes. In: Proceedings of the International Multiconference on Computer Science and Information Technology, Wisla, Poland (2010)Google Scholar
  6. 6.
    Yang, Z., Xu, H., Deng, J.: Robust and fast decoding of high-capacity color QR codes for mobile applications. Technical report (2017). arXiv:1704.06447
  7. 7.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.East Carolina UniversityGreenvilleUSA

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