Reading Vehicular Messages from Smart Road Signs: A Novel Method to Support Vehicle-to-Infrastructure in Rural Settings

Abstract

Vehicle-to-infrastructure (V2I) communication is essential for reliable deployment of connected automated vehicle technology, contributing to the advanced safety and optimization of our transportation networks. However, supplying and maintaining necessary wireless infrastructure is a challenging task, particularly when it comes to rural areas. This study proposes a novel methodology that uses artificial intelligence, machine vision, and smart traffic signs to support V2I in areas where availability of wireless communication infrastructure is limited. The objective of this paper is to investigate the operational challenges of the proposed low-cost solution in different V2I applications, including a MapData message in an unsignalized traffic intersection, traveler information message in a work zone, and a red-light violation warning with the help of a smart sign. The proposed system showed some important advantages, such as invulnerability to third-party alterations and robust operation under harsh environmental conditions.

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References

Federal Highway Administration. Urban Congestion Report. FHWA, Washington, D.C., 2016.

Bureau of Transportation Statistics. National Transportation Statistics 2018. USDOT, BTS, Washington, D.C., 2018.

Cheng

Zhang

Shen

Mark

J. W.

Connected Vehicles: Solutions and Challenges. IEEE Internet of Things Journal, Vol. 1, No. 4, 2014, pp. 289–299. https://doi.org/10.1109/JIOT.2014.2327587.

Mahdavian

Shojaei

Oloufa

Service Level Evaluation of Florida’s Highways Considering the Impact of Autonomous Vehicles. Proc., 36th International Symposium on Automation and Robotics in Construction (ISARC 2019). IAARC Publications, Waterloo, Ontario, Canada, Vol. 36, 2019, pp. 1163–1170.

Zhao

Zhang

M. H.

Wang

DSRC versus 4G-LTE for Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance. Journal of Advanced Transportation, Vol. 2017, 2017, pp. 1–10. https://doi.org/10.1155/2017/2750452.

Litman

Autonomous Vehicle Implementation Predictions Implications for Transport Planning. Victoria Transport Policy Institute, BC, 2019.

Douma

Petersen

Scenarios and Justification for Automated Vehicle Demonstration in Rural Minnesota. Publication CTS 19-18. Roadway Safety Institute, Minneapolis, MN, 2019.

Balke

Lukuc

Kuhn

Burris

Zmud

Morgan

Passetti

Dowling

Morrison

Marsters

Szyrnkowski

Mobility, Environment, and Public Agency Efficiency Refined Evaluation Plan—Tampa. Report No. FHWA-JPO-18-655. FHWA, Washington, D.C., 2018.

Balke

Lukuc

Kuhn

Burris

Zmud

Morrison

Marsters

Szyrnkowski

Mobility, Environment, and Public Agency Efficiency Refined Evaluation Plan—New York City. Report No. FHWA-JPO-18-654. FHWA, Washington, D.C., 2018.

10.

Bennett

Marsters

Szyrnkowski

Balke

Mobility, Environmental, and Public Agency Efficiency (MEP) Refined Evaluation Plan—Wyoming. Report No. FHWA-JPO-18-653. FHWA, Washington, D.C., 2018.

11.

Bezzina

Sayer

Safety Pilot Model Deployment Test Conductor Team Report. Report No. DOT HS 812 171. USDOT, NHTSA, Washington, D.C., 2015.

12.

Zhang

Cao

Bao

Tan

A Method for Connected Vehicle Trajectory Prediction and Collision Warning Algorithm Based on V2V Communication. International Journal of Crashworthiness, Vol. 22, No. 1, 2017, pp. 15–25. https://doi.org/10.1080/13588265.2016.1215584.

13.

Zhao

Wagh

Hou

Hulme

Qiao

Sadek

A. W.

Integrated Traffic-Driving-Networking Simulator for the Design of Connected Vehicle Applications: Eco-Signal Case Study. Journal of Intelligent Transportation Systems, Vol. 20, No. 1, 2016, pp. 75–87. https://doi.org/10.1080/15472450.2014.889920.

14.

Wada

Hsu

Kamijo

Vehicle Self-Localization in Urban Canyon Using 3D Map Based GPS Positioning and Vehicle Sensors. Proc., 2014 International Conference on Connected Vehicles and Expo (ICCVE), Vienna, Austria, 2014, pp. 792–798. https://doi.org/10.1109/ICCVE.2014.7297660.

15.

J. I.

Avedisov

S. S.

C. R.

Qin

W. B.

Sadeghpour

Orosz

Experimental Validation of Connected Automated Vehicle Design among Human-Driven Vehicles. Transportation Research Part C: Emerging Technologies, Vol. 91, 2018, pp. 335–352. https://doi.org/10.1016/j.trc.2018.04.005.

16.

Kamalanathsharma

R. K.

Rakha

H. A.

Badillo

Simulation Testing of Connected Vehicle Applications in a Cloud-Based Traffic Simulation Environment. Transportation Research Board, Washington, D.C., 2014.

17.

Lorenzi

Vaidya

Chun

Shafiq

Atluri

Enhancing the Government Service Experience through QR Codes on Mobile Platforms. Government Information Quarterly, Vol. 31, No. 1, 2014, pp. 6–16. https://doi.org/10.1016/J.GIQ.2013.05.025.

18.

Kruizinga

Petkov

Grigorescu

S. E.

Comparison of Texture Features Based on Gabor Filters. EEE Transactions on Image Processing, Vol. 11, No. 10, 2002, pp. 1160–1167. https://doi.org/10.1109/TIP.2002.804262.

19.

You’ve Been Haacked. Identicons as Graphical Digital Fingerprints. 2007. https://haacked.com/archive/2007/01/22/Identicons_as_Visual_Fingerprints.aspx/. Accessed November 12, 2020.

20.

Gangone

M. V.

Whelan

M. J.

Janoyan

K. D.

Jha

Field Deployment of a Dense Wireless Sensor Network for Condition Assessment of a Bridge Superstructure. Smart Sensor Phenomena Technology Networks and Systems 2008, Vol. 6933, 2008, p. 69330L. https://doi.org/10.1117/12.776604.

21.

Kato

Tokunaga

Maruyama

Maeda

Hirabayashi

Kitsukawa

Monrroy

Ando

Fujii

Azumi

Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems. Proc., 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), Porto, Portugal, 2018, pp. 287–296. https://doi.org/10.1109/ICCPS.2018.00035.

22.

Allinson

N. M.

A Comprehensive Review of Current Local Features for Computer Vision. Neurocomputing, Vol. 71, No. 10–12, 2008, pp. 1771–1787. https://doi.org/10.1016/j.neucom.2007.11.032.

23.

Lowe

D. G.

Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, Vol. 60, 2004, pp. 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.

24.

Bay

Tuytelaars

Van Gool

SURF: Speeded–Up Robust Features. In Computer Vision–ECCV 2006. Lecture Notes in Computer Science ( Leonardis

Bischof

Pinz

eds), Springer, Berlin, Vol. 3951, 2006. https://doi.org/10.1007/11744023_32.

25.

Leutenegger

Chli

Siegwart

R. Y.

BRISK: Binary Robust Invariant Scalable Keypoints. Proc., 2011 International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 2548–2555. https://doi.org/10.1109/ICCV.2011.6126542.

26.

Rublee

Rabaud

Konolige

Bradski

ORB: An Efficient Alternative to SIFT or SURF. Proc., 2011 International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 2564–2571. https://doi.org/10.1109/ICCV.2011.6126544.

27.

Min

Zhao

Zhang

Robust Features and Accurate Inliers Detection Framework: Application to Stereo Ego-Motion Estimation. KSII Transactions on Internet and Information Systems, Vol. 11, No. 1, 2017, pp. 302–320. https://doi.org/10.3837/tiis.2017.01.016.

28.

Muja

Lowe

D. G.

Fast Matching of Binary Features. Proc., 9th Conference on Computer and Robot Vision, Toronto, ON, Canada, 2012, pp. 404–410. https://doi.org/10.1109/CRV.2012.60.

29.

USDOT. Connected Vehicle ISD Message Creator. 2014. https://webapp.connectedvcs.com/isd/. Accessed November 25, 2020.