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Intersection Safety through Traffic Violation Detection

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Authors:
Mukremin Ozkul
10.5120/ijca2019919508

Mukremin Ozkul. Intersection Safety through Traffic Violation Detection. International Journal of Computer Applications 177(10):1-6, October 2019. BibTeX

@article{10.5120/ijca2019919508,
	author = {Mukremin Ozkul},
	title = {Intersection Safety through Traffic Violation Detection},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2019},
	volume = {177},
	number = {10},
	month = {Oct},
	year = {2019},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume177/number10/30930-2019919508},
	doi = {10.5120/ijca2019919508},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper presents a violation detection and collision avoidance system to vehicles at road intersections. The system relies on secure messages to assist vehicles in a constant size neighborhood by detecting traffic rule violations and finding trajectory conflicts at intersections. Each vehicle is modeled as an automaton that, regardless of its visual range, can see the states of other vehicles through on-board sensors and/or by using wireless communications. Traffic rules are encoded in the program of a vehicle and guide the changing state of the vehicle in traffic. Each vehicle can autonomously decide if the vehicles in its neighborhood comply with the traffic rules by observing the movements of the vehicles on the road and then anonymously reporting the observed violations to a traffic authority be further processed. Each vehicle periodically generates shared secrets which are then used as part of messages it sends to achieve security and privacy. The location of these messages is not traceable by any single traffic authority in the system, including the authentication parties and the road infrastructure. Yet, the proposed system is able find the location and real identity of any vehicle whenever it commits a rule violation in traffic with a lightweight protocol. Further, the security analysis is provided and the performance simulation results show that the system allows no false positives.

References

  1. National Highway Traffic Safety Administration. Traffic safety facts 2014.a compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. 2014.
  2. Nawal Aliou, Aouatif Amine, and Mohammed Rziza. Drivers fatigue detection based on yawning extraction. International Journal of Vehicular Technology, 2014, 2014.
  3. Martin Eriksson and Nikolaos P Papanikolopoulos. Driver fatigue: a vision-based approach to automatic diagnosis. Transportation Research Part C: Emerging Technologies, 9(6):399 – 413, 2001.
  4. Qiang Ji, Zhiwei Zhu, and P. Lan. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technology, 53(4):1052–1068, July 2004.
  5. Nidhi Sharma and V. K. Banga. Drowsiness warning system using artificial intelligence, 2010.
  6. S. Boonmee and P. Tangamchit. Portable reckless driving detection system. In 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, volume 01, pages 412– 415, May 2009.
  7. Cheol Oh, Eunbi Jung, Heesub Rim, Kyungpyo Kang, and Younsoo Kang. Intervehicle safety warning information system for unsafe driving events. Transportation Research Record: Journal of the Transportation Research Board, 2324:1–10, 2012.
  8. Rui Sun, Washington Yotto Ochieng, and Shaojun Feng. An integrated solution for lane level irregular driving detection on highways. Transportation Research Part C: Emerging Technologies, 56:61 – 79, 2015.
  9. Jesse Levinson, Michael Montemerlo, and Sebastian Thrun. Map-based precision vehicle localization in urban environments. In Robotics: Science and Systems, 2007.
  10. Jason J. Haas and Yih-Chun Hu. Communication requirements for crash avoidance. In Proceedings of the Seventh ACM InternationalWorkshop on VehiculAr InterNETworking, VANET ’10, pages 1–10. ACM, 2010.
  11. Kurt Dresner and Peter Stone. Multiagent traffic management: An improved intersection control mechanism. In Frank Dignum, Virginia Dignum, Sven Koenig, Sarit Kraus, Munindar P. Singh, and MichaelWooldridge, editors, The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, New York, NY, July 2005. ACM Press.
  12. Q. Jin, G. Wu, K. Boriboonsomsin, and M. Barth. Advanced intersection management for connected vehicles using a multi-agent systems approach. In 2012 IEEE Intelligent Vehicles Symposium, pages 932–937, June 2012.
  13. Matteo Vasirani and Sascha Ossowski. Learning and coordination for autonomous intersection control. Applied Artificial Intelligence, 25(3):193–216, 2011.
  14. S. Noh. Decision-making framework for autonomous driving at road intersections: Safeguarding against collision, overly conservative behavior, and violation vehicles. IEEE Transactions on Industrial Electronics, 66(4):3275–3286, April 2019.
  15. K. Shim. CPAS: An efficient conditional privacy-preserving authentication scheme for vehicular sensor networks. IEEE Transactions on Vehicular Technology, 61(4):1874–1883, May 2012.
  16. C. Zhang, R. Lu, X. Lin, P. . Ho, and X. Shen. An efficient identity-based batch verification scheme for vehicular sensor networks. In IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, pages 246–250, April 2008.
  17. Y. Sun, R. Lu, X. Lin, X. Shen, and J. Su. An efficient pseudonymous authentication scheme with strong privacy preservation for vehicular communications. IEEE Transactions on Vehicular Technology, 59(7):3589–3603, Sep. 2010.
  18. M. Ozkul and I. Capuni. An autonomous driving framework with self-configurable vehicle clusters. In 2014 International Conference on Connected Vehicles and Expo (ICCVE), pages 463–468, 2014.
  19. Ieee standard for information technology– local and metropolitan area networks– specific requirements– part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications amendment 6: Wireless access in vehicular environments. IEEE Std 802.11p-2010 (Amendment to IEEE Std 802.11-2007 as amended by IEEE Std 802.11k- 2008, IEEE Std 802.11r-2008, IEEE Std 802.11y-2008, IEEE Std 802.11n-2009, and IEEE Std 802.11w-2009), pages 1–51, July 2010.
  20. Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz. Sumo - simulation of urban mobility: An overview. In in SIMUL 2011, The Third International Conference on Advances in System Simulation, pages 63–68, 2011.
  21. Andr´as Varga and Rudolf Hornig. An overview of the omnet++ simulation environment. In Proceedings of the 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems & Workshops, pages 60:1–60:10, 2008.
  22. C. Sommer, R. German, and F. Dressler. Bidirectionally coupled network and road traffic simulation for improved ivc analysis. IEEE Transactions on Mobile Computing, 10(1):3– 15, 2011.
  23. Axel Wegener, MichalPi´orkowski, Maxim Raya, Horst Hellbr¨uck, Stefan Fischer, and Jean-Pierre Hubaux. Traci: An interface for coupling road traffic and network simulators. In Proceedings of the 11th Communications and Networking Simulation Symposium, CNS ’08, pages 155–163, New York, NY, USA, 2008. ACM.

Keywords

Vehicular ad hoc networks (VANETs),Dedicated Short Range Communications(DSRC), traffic violation and ticketing, traffic safety, location privacy