Work zones, being a critical component of roadway transportation systems, can benefit greatly from computer vision-enabled roadway infrastructures, specifically in connected vehicle (CV) environments. Connected infrastructures, such as roadside units (RSU) and on-board units (OBU), can greatly improve the environmental awareness and safety of CVs driving through a work zone. The contribution of this paper lies in developing a vision-based approach to generate work zone safety messages in real time, utilizing video streams from roadside monocular traffic cameras that can be used by CV work zone safety apps on mobile devices to reliably navigate through a work zone. A monocular traffic camera calibration method is proposed to establish the accurate mapping between the image plane and Global Position System (GPS) space. Real test scenarios show that our algorithm can precisely and effectively locate work zone boundaries from a monocular traffic camera in real time. We demonstrate the capabilities and features of our system through real-world experiments where the driver cannot see the work zone. End-to-end latency analysis reveals that the vision-based work zone safety warning system satisfies the active safety latency requirements. This vision-based work zone safety alert system ensures the safety of both the worker and the driver in a CV environment.
2015 Motor Vehicle Crashes: Overview. Traffic Safety Facts: Research Note. U.S. National Highway Traffic Safety Administration, Washington, D. C., 2016, pp. 1–9.
2.
RadwanE.HarbR.RamasamyS.Evaluation of Safety and Operational Effectiveness of Dynamic Lane Merge System in Florida. Final Report. Florida Department of Transportation, Tallahassee, April2009.
3.
SchrockS. D.UllmanG. L.CothronA. S.KrausE.VoigtA. P.An Analysis of Fatal Work Zone Crashes in Texas. Report FHW A/TX-05/0-4028. Texas A&M Transportation Institute, College Station, October2004.
4.
DanielJ.DixonK.JaredD.Analysis of Fatal Crashes in Georgia Work Zones. Transportation Research Record: Journal of the Transportation Research Board, 2000. 1715: 18–23.
5.
GarberN. J.ZhaoM.Distribution and Characteristics of Crashes at Different Work Zone Locations in Virginia. Transportation Research Record: Journal of the Transportation Research Board, 2002. 1794: 19–25.
6.
NemethZ. A.MigletzD. J.Accident Characteristics Before, During, and After Safety Upgrading Projects on Ohio’s Rural Interstate System. Transportation Research Record: Journal of the Transportation Research Board, 1978. 672: 19–23.
7.
HallJ. W.LorenzV. M.Characteristics of Construction-Zone Accidents. Transportation Research Record: Journal of the Transportation Research Board, 1989. 1230: 20–27.
8.
RouphailN. M.YangZ. S.FazioJ.Comparative Study of Short- and Long-Term Urban Freeway Work Zones. Transportation Research Record: Journal of the Transportation Research Board, 1988. 1163: 4–14.
9.
DehmanA.FarooqB.Are Work Zones and Connected Automated Vehicles Ready for a Harmonious Coexistence? A Scoping Review and Research Agenda. Transportation Research Part C: Emerging Technologies, Vol. 133, 2021, p. 103422.
10.
WuX.SubramanianS.GuhaR.WhiteR. G.LiJ.LuK. W.BucceriA.ZhangT.Vehicular Communications Using DSRC: Challenges, Enhancements, and Evolution. IEEE Journal on Selected Areas in Communications, Vol. 31, No. 9, 2013, pp. 399–408.
11.
RaddaouiO.AhmedM. M.GaweeshS. M.Assessment of the Effectiveness of Connected Vehicle Weather and Work Zone Warnings in Improving Truck Driver Safety. IATSS Research, Vol. 44, No. 3, 2020, pp. 230–237.
12.
BenekohalR. F.ShimE. U.ResendeP. T.Truck Drivers’ Concerns in Work Zones: Travel Characteristics and Accident Experiences. Transportation Research Record: Journal of the Transportation Research Board, 1995. 1509: 55–64.
13.
HanW.WhiteE.MollenhauerM.Roofigari-EsfahanN.A Connected Work Zone Hazard Detection System for Roadway Construction Workers. Proc., Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, American Society of Civil Engineers, Reston, VA, June13, 2019, pp. 242–250.
14.
SchönrockR.WolfF.RussT.Smart Traffic Cone-Dynamic Detection and Localization of Traffic Disruptions. Proc., FAST-zero’15: 3rd International Symposium on Future Active Safety Technology Toward Zero Traffic Accidents, Gothenburg, Sweden, 2015.
AbodoF.RittmullerR.SumnerB.BerthaumeA.Detecting Work Zones in SHRP 2 NDS Videos Using Deep Learning Based Computer Vision. Proc., 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, December 17, 2018, IEEE, New York, pp. 679–686.
17.
SeoY. W.LeeJ.ZhangW.WettergreenD.Recognition of Highway Workzones for Reliable Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 2, 2014, pp. 708–718.
18.
MathibelaB.OsborneM. A.PosnerI.NewmanP.Can Priors Be Trusted? Learning to Anticipate Roadworks. Proc., 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, September 16, 2012, IEEE, New York, pp. 927–932.
19.
KunzP.SchreierM.Automated Detection of Construction Sites on Motorways. Proc., IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, IEEE, New York, June 11, 2017, pp. 1378–1385.
20.
GrafR.WimmerA.DietmayerK. C.Probabilistic Estimation of Temporary Lanes at Road Work Zones. Proc., 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, IEEE, New York, September 16, 2012, pp. 716–721.
21.
ShiW.RajkumarR. R.Work Zone Detection for Autonomous Vehicles. Proc., IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, IEEE, New York, September 19, 2021, pp. 1585–1591.
22.
GirshickR.DonahueJ.DarrellT.MalikJ.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proc., IEEE Conference on Computer Vision and Pattern Recognition, IEEE, New York, 2014, pp. 580–587.
23.
GirshickR.Fast R-CNN. Proc., IEEE International Conference on Computer Vision, IEEE, New York, 2015, pp. 1440–1448.
24.
RenS.HeK.GirshickR.SunJ.Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Advances in Neural Information Processing Systems 28 (CortesC.LawrenceN. D.LeeD. D.SugiyamaM.GarnettR., eds.), Curran Associates, Inc., San Francisco, 2015.
25.
LiuW.AnguelovD.ErhanD.SzegedyC.ReedS.FuC. Y.BergA. C.SSD: Single Shot Multibox Detector. Proc., Computer Vision–ECCV 2016: 14th European Conference, Part I, Amsterdam, The Netherlands, October 11–14, 2016, Springer International Publishing, Cham, Switzerland, pp. 21–37.
RedmonJ.FarhadiA.YOLO9000: Better, Faster, Stronger. Proc., IEEE Conference on Computer Vision and Pattern Recognition, IEEE, New York, 2017, pp. 7263–7271.
GendersW.RazaviS. N.Impact of Connected Vehicle on Work Zone Network Safety Through Dynamic Route Guidance. Journal of Computing in Civil Engineering, Vol. 30, No. 2, 2016, p. 04015020.
30.
DeyK. C.RayamajhiA.ChowdhuryM.BhavsarP.MartinJ.Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) Communication in a Heterogeneous Wireless Network–Performance Evaluation. Transportation Research Part C: Emerging Technologies, Vol. 68, 2016, pp. 168–184.
31.
AbdulsattarH.MostafiziA.WangH.Surrogate Safety Assessment of Work Zone Rear-End Collisions in a Connected Vehicle Environment: Agent-Based Modeling Framework. Journal of Transportation Engineering, Part A: Systems, Vol. 144, No. 8, 2018, p. 04018038.
MishraS.GoliasM. M.ThapaD.Work Zone Alert Systems. Tennessee Department of Transportation, Nashville, April1, 2021.
34.
QiaoF.RahmanR.LiQ.YuL.Safe and Environment-Friendly Forward Collision Warning Messages in the Advance Warning Area of a Construction Zone. International Journal of Intelligent Transportation Systems Research, Vol. 15, 2017, pp. 166–179.
35.
IslamM.RahmanM.ChowdhuryM.ComertG.SoodE. D.AponA.Vision-Based Personal Safety Messages (PSMs) Generation for Connected Vehicles. IEEE Transactions on Vehicular Technology, Vol. 69, No. 9, 2020, pp. 9402–9416.
36.
V2X Core Technical Committee. Dedicated Short Range Communications (DSRC) Message Set Dictionary™. SAE International, Warrendale, PA, March2016.
WenT.XiaoZ.JiangK.YangM.LiK.YangD.High Precision Target Positioning Method for RSU in Cooperative Perception. Proc., IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), Kuala Lumpur, Malaysia, IEEE, New York, September 27, 2019, pp. 1–6.
39.
LinT. Y.MaireM.BelongieS.HaysJ.PeronaP.RamananD.DollárP.ZitnickC. L.Microsoft COCO: Common Objects in Context. Proc., Computer Vision–ECCV 2014: 13th European Conference, Part V, Zurich, Switzerland, September 6–12, 2014, Springer International Publishing, Cham, 2014, pp. 740–755.
Brady, J., and D. Dudley. The Work Zone Data Exchange (WZDX) Specification. [Internet]. GitHub. https://github.com/usdot-jpo-ode/wzdx. Accessed February 6, 2023.
RauchA.KlannerF.DietmayerK.Analysis of V2X Communication Parameters for the Development of a Fusion Architecture for Cooperative Perception Systems. Proc., IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, IEEE, New York, June 5, 2011, pp. 685–690.
46.
RauchA.KlannerF.RasshoferR.DietmayerK.Car2X-Based Perception in a High-Level Fusion Architecture for Cooperative Perception Systems. Proc., IEEE Intelligent Vehicles Symposium, Madrid, Spain, IEEE, New York, June 3, 2012, pp. 270–275.
47.
RauchA.MaierS.KlannerF.DietmayerK.Inter-Vehicle Object Association for Cooperative Perception Systems. Proc., 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands, IEEE, New York, October 6, 2013, pp. 893–898.
48.
GüntherH. J.MennengaB.TrauerO.RieblR.WolfL.Realizing Collective Perception in a Vehicle. Proc., IEEE Vehicular Networking Conference (VNC), Columbus, OH, IEEE, New York, December 8, 2016, pp. 1–8.
49.
ShanM.NarulaK.WorrallS.WongY. F.PerezJ. S.GrayP.NebotE.A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception. Proc., IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, IEEE, New York, October 8, 2022, pp. 2013–2020.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
