References
[1] “Global status report on road safety 2018,” World Health Organization, 19-Jun-2020. [Online]. Available: https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/. [Accessed: 03-Aug-2020].
[2]M. Maile and L. Delgrossi, “Cooperative Intersection Collision Avoidance System For Violations(CICAS-V) For Avoidance Of Violation-Based Intersection Crashes,” 2009.
[3]F. Basma, Y. Tachwali and H. H. Refai, “Intersection collision avoidance system using infrastructure communication,” 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, 2011, pp. 422-427, doi: 10.1109/ITSC.2011.6083007.
[4]S. Lefèvre, J. Petit, R. Bajcsy, C. Laugier and F. Kargl, “Impact of V2X privacy strategies on Intersection Collision Avoidance systems,” 2013 IEEE Vehicular Networking Conference, Boston, MA, 2013, pp. 71-78, doi: 10.1109/VNC.2013.6737592.
[5]M. R. Hafner, D. Cunningham, L. Caminiti and D. Del Vecchio, “Cooperative Collision Avoidance at Intersections: Algorithms and Experiments,” in IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1162-1175, Sept. 2013, doi: 10.1109/TITS.2013.2252901.
[6]National Highway Traffic Safety Administration, “Crash Factors in Intersection-Related Crashes: An On-Scene Perspective,” Sep-2010. [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811366.
[7] “Top 10 causes of death,” World Health Organization, 06-May-2019. [Online]. Available: https://www.who.int/gho/mortality_burden_disease/causes_death/top_10/en/.
[8]S. Kumar, G. Bansal and V. S. Shekhawat, “A Machine Learning Approach for Traffic Flow Provisioning in Software Defined Networks,” 2020 International Conference on Information Networking (ICOIN), Barcelona, Spain, 2020, pp. 602-607, doi: 10.1109/ICOIN48656.2020.9016529.
[9] P. Sun, N. Aljeri and A. Boukerche, “Machine Learning-Based Models for Real-time Traffic Flow Prediction in Vehicular Networks,” in IEEE Network, vol. 34, no. 3, pp. 178-185, May/June 2020, doi: 10.1109/MNET.011.1900338.
[10] A. Viloria, O. B. P. Lezama, N. Varela, and J. L. D. Martínez, “Optimization of Driving Efficiency for Pre-determined Routes: Proactive Vehicle Traffic Control,” Communications in Computer and Information Science Computing Science, Communication and Security, vol. 1235, pp. 82–94, 2020.
[11]Z. Shen, W. Wang, Q. Shen, S. Zhu, H. M. Fardoun, and J. Lou, “A novel learning method for multi-intersections aware traffic flow forecasting,” Neurocomputing, vol. 398, pp. 477–484, Jul. 2020.
[12]A. Jamal, M. Tauhidur Rahman, H. M. Al-Ahmadi, I. Ullah, and M. Zahid, “Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms,” Sustainability, vol. 12, 2020.
[13]M. Pasha, M. U. Farooq, T. Yasmeen, and K. U. R. Khan, “Vehicular Collision Avoidance at Intersection Using V2I Communications for Road Safety,” Innovations in Computer Science and Engineering Lecture Notes in Networks and Systems, vol. 103, pp. 23–31, 2020.
[14]Junction-Centric Data Forwarding in Urban Vehicular Communication: Future Direction and Challenges, vol. 884, Dec. 2019.
[15]“Traffic Control Systems Handbook,” Traffic Control Systems Handbook – FHWA Office of Operations. [Online]. Available: https://ops.fhwa.dot.gov/publications/fhwahop06006/index.htm.
[16]O. Erçak?r, O. K?z?l?rmak, and V. Erol, “Network Security Issues and Solutions on Vehicular Communication Systems,” Preprints, 01-Jun-2017. [Online]. Available: https://www.preprints.org/manuscript/201706.0001/v1.
[17]“Unit Cost Entries for Inductive Loop Surveillance at Intersection,” U.S. Department of Transportation. [Online]. Available: https://www.itscosts.its.dot.gov/its/benecost.nsf/DisplayRUCByUnitCostElementUnadjusted?ReadForm&UnitCostElement=Inductive Loop Surveillance at Intersection &Subsystem=Roadside Detection (RS-D).
[18]J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv:1804.02767 , vol. 1, Apr. 2018.
[19]YunYang1994, “YunYang1994/tensorflow-yolov3,” GitHub, 01-Dec-2018. [Online]. Available: https://github.com/YunYang1994/tensorflow-yolov3.
[20]Motchallenge.net. 2017. MOT Challenge – Data. [online]
Available: https://motchallenge.net/data/MOT17/.
