Numerous sensors were introduced to intelligent transportation systems (ITS) in the past decade. Consequently, new sensing technologies and the generated data attracted more and more attention, which brought new challenges, such as redundant sensors, huge maintenance costs, and data explosion, to ITS. To satisfy the core demands of traffic agencies in a more effective and efficient way, therefore, this paper proposes the idea of “Sensing as a Service” (SaaS) and implements it in the Cooperative and Comprehensive Smart Edge Node for Sensing and OpeRation (COCO SENSOR) system for practical deployments. COCO SENSOR is an innovative multi-task sensing system, which was developed to address key practical applications, including real-time vehicle counting and recognition, road-surface-condition classification, visibility estimation, and live communication among traffic controllers and road users in a single unit. COCO SENSOR introduced customized cooperative sensing and a parallel computation mechanism to increase perception accuracy and efficiency with limited computation resources on the edge device. In collaboration with the Washington State Department of Transportation and the City of Bellevue, a field experiment was conducted to test the system’s performance. The results of the experiment show that the COCO SENSOR effectively fills the gap between sensing and traffic services and that it successfully executed four practical applications, including traffic-volume counting by vehicle type, traffic-status detection, road-visibility estimation, and road-surface-condition classification, with high accuracy. Additionally, a mobile app was developed for both traffic managers and users to access comprehensive traffic information and live warning messages.
ITS America, Department of Transportation, and Intelligent Transportation Systems Joint Program Office. The National Architecture for ITS: A Framework for Integrated Transportation into the 21st Century. Tech. Rep., United States Department of Transportation, Washington, DC, 1996.
2.
GreerL.FraserJ. L.HicksD.MercerM.ThompsonK.Intelligent Transportation Systems Benefits, Costs, and Lessons Learned: 2018 Update Report. Technical Report, United States Department of Transportation, ITS Joint Program Office, Washington, DC, 2018.
3.
El FaouziN.-E.LeungH.KurianA.Data Fusion in Intelligent Transportation Systems: Progress and Challenges—A Survey. Information Fusion, Vol. 12, No. 1, 2011, pp. 4–10.
4.
LiuC.YangH.KeR.WangY.Toward a Dynamic Reversible Lane Management Strategy by Empowering Learning-Based Predictive Assignment Scheme. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 12, 2022, pp. 23311–23323.
5.
FuX.YangH.LiuC.WangJ.WangY.A Hybrid Neural Network for Large-Scale Expressway Network OD Prediction Based on Toll Data. PLoS One, Vol. 14, No. 5, 2019, p. e0217241.
6.
KeR.LiuC.YangH.SunW.WangY.Real-Time Traffic and Road Surveillance with Parallel Edge Intelligence. IEEE Journal of Radio Frequency Identification, Vol. 6, 2022, pp. 693–696.
7.
ZhouZ.ChenX.LiE.ZengL.LuoK.ZhangJ.Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing. Proceedings of the IEEE, Vol. 107, No. 8, 2019, pp. 1738–1762.
8.
DjahelS.DoolanR.MunteanG.-M.MurphyJ.A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches. IEEE Communications Surveys & Tutorials, Vol. 17, No. 1, 2014, pp. 125–151.
9.
YangH. F.CaiJ.LiuC.KeR.WangY.Cooperative Multi-Camera Vehicle Tracking and Traffic Surveillance with Edge Artificial Intelligence and Representation Learning. Transportation Research Part C: Emerging Technologies, Vol. 148, 2023, p. 103982.
10.
ZhuM.YangH. F.LiuC.PuZ.WangY.Real-Time Crash Identification Using Connected Electric Vehicle Operation Data. Accident Analysis & Prevention, Vol. 173, 2022, p. 106708.
11.
KleinL. A.ITS Sensors and Architectures for Traffic Management and Connected Vehicles. CRC Press, Boca Raton, FL, 2017.
12.
TsaiM.-J.CuiZ.LiuC.YangH.WangY.An Incremental Learning-Based Framework for Non-Stationary Traffic Representations Clustering and Forecasting. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, Macau, China, 2022, pp. 3237–3242.
13.
YangH.LiuC.ZhuM.BanX.SunW.WangY.How fast you will drive? predicting speed of customized paths by deep neural network. IEEE transactions on intelligent transportation systems, Vol. 23, No. 3, 2021, pp. 2045–2055.
14.
WangY.SunW.LiuC.Data for Cooperative Perception of Road-Side Unit and On-Board Equipment with Edge Artificial Intelligence for Driving Assistance [supporting dataset]. https://doi.org/10.5281/zenodo.5738251
15.
LiuC.Bi-Level Optimization Algorithm for Dynamic Reversible Lane Control Based on Short-Term Traffic Flow Prediction. University of Washington ProQuest Dissertations Publishing, Seattle, WA, 2020.
16.
De SouzaA. M.BrennandC. A.YokoyamaR. S.DonatoE. A.MadeiraE. R.VillasL. A.Traffic Management Systems: A Classification, Review, Challenges, and Future Perspectives. International Journal of Distributed Sensor Networks, Vol. 13, No. 4, 2017, p. 1550147716683612.
17.
ZhouX.KeR.YangH.LiuC.When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges. Applied Sciences, Vol. 11, No. 20, 2021, p. 9680.
18.
OliverN. M.RosarioB.PentlandA. P.A Bayesian Computer Vision System for Modeling Human Interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, 2000, pp. 831–843.
19.
BabaeiP.Vehicles Tracking and Classification Using Traffic Zones in a Hybrid Scheme for Intersection Traffic Management by Smart Cameras. 2010 International Conference on Signal and Image Processing, IEEE, Chennai, India, 2010, pp. 49–53.
20.
Felguera-MartínD.González-PartidaJ.-T.Almorox-GonzálezP.Burgos-GarcíaM.Vehicular Traffic Surveillance and Road Lane Detection Using Radar Interferometry. IEEE Transactions on Vehicular Technology, Vol. 61, No. 3, 2012, pp. 959–970.
21.
WangY.SunW.LiuC.CuiZ.ZhuM.PuZ., et al. Cooperative Perception of Roadside Unit and Onboard Equipment with Edge Artificial Intelligence for Driving Assistance. 2021. https://rosap.ntl.bts.gov/view/dot/60635
22.
HanS.MaoH.DallyW. J.Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv Preprint arXiv:1510.00149, 2015.
23.
FerdowsiA.ChallitaU.SaadW.Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview. IEEE Vehicular Technology Magazine, Vol. 14, No. 1, 2019, pp. 62–70.
24.
BochkovskiyA.WangC.-Y.LiaoH.-Y. M.YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv Preprint arXiv:2004.10934, 2020.
25.
TanM.LeQ.Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks. Proc., 36th International Conference on Machine Learning. ICML, Long Beach, 2019, pp. 6105–6114.
26.
LingX.ShengJ.BaiocchiO.LiuX.TolentinoM. E.Identifying Parking Spaces & Detecting Occupancy Using Vision-Based IoT Devices. 2017 Global Internet of Things Summit (GIoTS). IEEE, Geneva, Switzerland, 2017, pp. 1–6. DOI: 10.1109/GIOTS.2017.8016227.
27.
ZhouJ.DaiH.-N.WangH.Lightweight Convolution Neural Networks for Mobile Edge Computing in Transportation Cyber Physical Systems. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 10, No. 6, 2019, pp. 1–20.
28.
ChenX.MaH.WanJ.LiB.XiaT.Multi-View 3D Object Detection Network for Autonomous Driving. Proc., IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2017, pp. 1907–1915.
29.
AeberhardM.PaulS.KaempchenN.BertramT.Object Existence Probability Fusion Using Dempster-Shafer Theory in a High-Level Sensor Data Fusion Architecture. Proc., 2011 IEEE Intelligent Vehicles Symposium (IV), IEEE, Baden-Baden, Germany, 2011, pp. 770–775.
30.
RauchA.KlannerF.DietmayerK.Analysis of V2X Communication Parameters for the Development of a Fusion Architecture for Cooperative Perception Systems. 2011 IEEE Intelligent Vehicles Symposium (IV). IEEE, Baden-Baden, Germany, 2011, pp. 685–690.
31.
McMahanB.MooreE.RamageD.HampsonS.y ArcasB. A.Communication-Efficient Learning of Deep Networks from Decentralized Data. Proc., 20th International Conference on Artificial Intelligence and Statistics. PMLR, Fort Lauderdale, Florida, 2017, pp. 1273–1282.
32.
TsukadaM.OiT.KitazawaM.EsakiH.Networked Roadside Perception Units for Autonomous Driving. Sensors, Vol. 20, No. 18, 2020, p. 5320.
33.
ChtourouA.MerdrignacP.ShagdarO.Collective Perception Service for Connected Vehicles and Roadside Infrastructure. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). IEEE, Helsinki, Finland, 2021, pp. 1–5.
34.
HeK.SunJ.TangX.Single Image Haze Removal Using Dark Channel Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, 2010, pp. 2341–2353.
35.
CannyJ.A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, 1986, pp. 679–698.
36.
RongW.LiZ.ZhangW.SunL.An Improved CANNY Edge Detection Algorithm. 2014 IEEE International Conference on Mechatronics and Automation, IEEE, Tianjin, China, 2014, pp. 577–582.
37.
SandlerM.HowardA.ZhuM.ZhmoginovA.ChenL.-C.MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proc., IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, 2018, pp. 4510–4520.
38.
LiuW.AnguelovD.ErhanD.SzegedyC.ReedS.FuC.-Y.BergA. C.SSD: Single Shot Multibox Detector. European Conference on Computer Vision. Springer, Cham, 2016, pp. 21–37.
39.
LinT.-Y.MaireM.BelongieS.HaysJ.PeronaP.RamananD.DollárP.ZitnickC. L.Microsoft COCO: Common Objects in Context. European Conference on Computer Vision. Springer, Cham, 2014, pp. 740–755.
40.
LuoZ.Branchaud-CharronF.LemaireC.KonradJ.LiS.MishraA.AchkarA.EichelJ.JodoinP.-M.MIOTCD: A New Benchmark Dataset for Vehicle Classification and Localization. IEEE Transactions on Image Processing, Vol. 27, No. 10, 2018, pp. 5129–5141.
