Applied Intelligence

, Volume 49, Issue 5, pp 1621–1635 | Cite as

Autonomous and connected intersection crossing traffic management using discrete-time occupancies trajectory

  • Qiang Lu
  • Kyoung-Dae KimEmail author


In this paper, we address the problem of safe and efficient intersection crossing traffic management of autonomous and connected ground traffic. Toward this objective, we propose an algorithm called the discrete-time occupancies trajectory based intersection traffic coordination algorithm (DICA). We show that the basic DICA has a computational complexity of \(\mathcal {O}(n^{2} {L_{m}^{3}})\) where n is the number of vehicles granted to cross an intersection and Lm is the maximum length of intersection crossing routes. To improve the overall computational efficiency of the algorithm, the basic DICA is enhanced by several computational approaches that are proposed in this paper. The enhanced algorithm has the computational complexity of \(\mathcal {O}(n^{2} L_{m} \log _{2} L_{m})\). The improved computational efficiency of the enhanced algorithm is validated through simulations using an open source traffic simulator called the simulation of urban mobility (SUMO). The overall throughput, as well as the computational efficiency of the enhanced algorithm, are also compared with those of an optimized traffic light control algorithm.


Autonomous vehicles Intelligent intersection management Discrete-time occupancies trajectory (DTOT) Computational complexity 


  1. 1.
    Azimi S, Bhatia G, Rajkumar R, Mudalige P (2013) Reliable intersection protocols using vehicular networks. In: 2013 ACM/IEEE international conference on cyber-physical systems (ICCPS). IEEE, pp 1–10Google Scholar
  2. 2.
    Bengler K, Dietmayer K, Farber B, Maurer M, Stiller C, Winner H (2014) Three decades of driver assistance systems: review and future perspectives. IEEE Intell Transp Syst Mag 6(4):6–22CrossRefGoogle Scholar
  3. 3.
    Board TR (2000) Highway capacity manual. National Academy of Sciences, Transportation Research Board, Washington DCGoogle Scholar
  4. 4.
    Carlino D, Boyles SD, Stone P (2013) Auction-based autonomous intersection management. In: 16th international IEEE conference on intelligent transportation systems (ITSC 2013). IEEE, pp 529–534Google Scholar
  5. 5.
    Cheng D, Tian ZZ, Messer CJ (2005) Development of an improved cycle length model over the highway capacity manual 2000 quick estimation method. J Transport Eng 131(12):890–897CrossRefGoogle Scholar
  6. 6.
    Colombo A, Del Vecchio D (2012) Efficient algorithms for collision avoidance at intersections. In: Proceedings of the 15th ACM international conference on hybrid systems: computation and control. ACM, pp 145–154Google Scholar
  7. 7.
    DARPA: The darpa urban challenge (2007).
  8. 8.
    Dresner K, Stone P (2008) A multiagent approach to autonomous intersection management. J Artif Intell Res 31:591–656CrossRefGoogle Scholar
  9. 9.
    Guler SI, Menendez M, Meier L (2014) Using connected vehicle technology to improve the efficiency of intersections. Transp Res Part C: Emerg Technol 46:121–131CrossRefGoogle Scholar
  10. 10.
    Horowitz R, Varaiya P (2000) Control design of an automated highway system. Proc IEEE 88(7):913–925CrossRefGoogle Scholar
  11. 11.
    Jin Q, Wu G, Boriboonsomsin K, Barth M (2012) Advanced intersection management for connected vehicles using a multi-agent systems approach. In: 2012 IEEE on intelligent vehicles symposium (IV). IEEE, pp 932–937Google Scholar
  12. 12.
    Kim KD, Kumar PR (2014) An mpc-based approach to provable system-wide safety and liveness of autonomous ground traffic. IEEE Trans Autom Control 59(12):3341–3356MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of sumo–simulation of urban mobility. Int J Adv Syst Measur, 5(3&4)Google Scholar
  14. 14.
    Lee J, Park B (2012) Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment. IEEE Trans Intell Transp Syst 13(1):81–90CrossRefGoogle Scholar
  15. 15.
    Li DP, Liu YJ, Tong S, Chen CP, Li DJ (2018) Neural networks-based adaptive control for nonlinear state constrained systems with input delay. IEEE Transactions on CyberneticsGoogle Scholar
  16. 16.
    Liu L, Liu YJ, Tong S (2018) Neural networks-based adaptive finite-time fault-tolerant control for a class of strict-feedback switched nonlinear systems. IEEE Transactions on CyberneticsGoogle Scholar
  17. 17.
    Liu YJ, Gong M, Tong S, Chen CP, Li DJ (2018) Adaptive fuzzy output feedback control for a class of nonlinear systems with full state constraints. IEEE Transactions on Fuzzy SystemsGoogle Scholar
  18. 18.
    Liu YJ, Lu S, Tong S, Chen X, Chen CP, Li DJ (2018) Adaptive control-based barrier lyapunov functions for a class of stochastic nonlinear systems with full state constraints. Automatica 87:83–93MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Lu Q, Kim KD (2016) Intelligent intersection management of autonomous traffic using discrete-time occupancies trajectory. J Traffic Logist Eng 4(1):1–6Google Scholar
  20. 20.
    Malikopoulos AA, Cassandras CG (2016) Decentralized optimal control for connected and automated vehicles at an intersection. arXiv:1602.03786
  21. 21.
    Markoff J (2010) Google cars drive themselves, in traffic. New York Times 10(A1):9Google Scholar
  22. 22.
    Miculescu D, Karaman S (2016) Polling-systems-based autonomous vehicle coordination in traffic intersections with no traffic signals. arXiv:1607.07896
  23. 23.
    Neto FDN, de Souza Baptista C, Campelo CE (2018) Combining Markov model and prediction by partial matching compression technique for route and destination prediction. Knowl-Based Syst 154:81–92CrossRefGoogle Scholar
  24. 24.
    Onieva E, Hernández-Jayo U, Osaba E, Perallos A, Zhang X (2015) A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving. Inform Sci 321:14–30CrossRefGoogle Scholar
  25. 25.
    Tientrakool P, Ho YC, Maxemchuk NF (2011) Highway capacity benefits from using vehicle-to-vehicle communication and sensors for collision avoidance. In: 2011 IEEE on vehicular technology conference (VTC Fall). IEEE, pp 1–5Google Scholar
  26. 26.
    Vasirani M, Ossowski S (2012) A market-inspired approach for intersection management in urban road traffic networks. J Artif Intell Res 43:621–659CrossRefzbMATHGoogle Scholar
  27. 27.
    Wang P, Li W, Li C, Hou Y (2018) Action recognition based on joint trajectory maps with convolutional neural networks. Knowledge-Based SystemsGoogle Scholar
  28. 28.
    Wu J, Abbas-Turki A, El Moudni A (2012) Cooperative driving: an ant colony system for autonomous intersection management. Appl Intell 37(2):207–222CrossRefGoogle Scholar
  29. 29.
    Wuthishuwong C, Traechtler A, Bruns T (2015) Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication. EURASIP J Wirel Commun Netw 2015(1):1–12CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of DenverDenverUSA
  2. 2.Department of Information and Communication EngineeringDGISTDaeguRepublic of Korea

Personalised recommendations