Advertisement

A Control Strategy for Vehicles in a Traffic Flow Aimed at the Fastest Safe Motion

  • Andrey M. ValuevEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)

Abstract

The paper discusses the issue of driving a car in a traffic flow that provides the highest speed under conditions of security guarantees. The basic assumption is an accurate knowledge of the speed of the controlled vehicle and its predecessor and the distance to it. The way to define the control law implementing the formulated objective is proposed for a separate vehicle as well as for a chain of connected autonomous vehicles. The results obtained can be used in two ways: (1) as a control program (in the “smart advice-tick” mode for a vehicle controlled by the driver and in the autopilot mode); (2) for estimating the maximum throughput of sections of the urban road network by computer simulation. In the latter case, an adequate means is the use of the obtained control as an element of the general “microscopic” model of traffic flow, which it is expedient to formulate in the form of a hybrid dynamical system—an event-switched process. Computational and information aspects as well as perspectives of the approach development are discussed.

Keywords

Vehicular traffic flow Safety Intelligent control system Control law Connected autonomous vehicles Road throughput 

References

  1. 1.
    Rashad, L.J., Hassan, F.A.: Artificial neural estimator and controller for field oriented control of three-phase I.M. Int. J. Intell. Syst. Appl. (IJISA), 11(6), 40–48 (2019)CrossRefGoogle Scholar
  2. 2.
    Agrawal, P., Agrawal, H.: Adaptive algorithm design for cooperative hunting in multi-robots. Int. J. Intell. Syst. Appl. (IJISA) 10(12), 47–55 (2018)Google Scholar
  3. 3.
    Elhoseny, M., Abdulaziz, S., Xiaohui, Y.: Optimizing robot path in dynamic environments using Genetic Algorithm and Bezier Curve. J. Intell. Fuzzy Syst. 33(4), 2305–2316 (2017)CrossRefGoogle Scholar
  4. 4.
    Dennouni, N., Peter, Y., Lancieri, L., Slama, Z.: Towards an incremental recommendation of POIs for mobile tourists without profiles. Int. J. Intell. Syst. Appl. (IJISA), 10(10), 42–52 (2018)CrossRefGoogle Scholar
  5. 5.
    Adebiyi, R.F.O., Abubilal, K.A., Tekanyi, A.M.S., Adebiyi, B.H.: Management of vehicular traffic system using Artificial Bee Colony Algorithm. Int. J. Image, Graph. Signal Process. (IJIGSP) 9(11), 18–28 (2017)CrossRefGoogle Scholar
  6. 6.
    Plessen, M.G., Bernardini, D., Esen, H., Bemporad, A.: Spatial-based predictive control and geometric corridor planning for adaptive cruise control coupled with obstacle avoidance. IEEE Trans. Control Syst. Technol. 26(1), 38–50 (2017)CrossRefGoogle Scholar
  7. 7.
    Wang, C., Gong, S., Zhou, A., Li, T., Peeta, S.: Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints. Transportation Research Part C: Emerging Technologies (2019, in press).  https://doi.org/10.1016/j.trc.2019.04.010
  8. 8.
    Zhou, C., Weng, Z., Chen, X., Zhizhe, S.: Integrated traffic information service system for public travel based on smart phones applications: a case in China. Int. J. Intell. Syst. Appl. (IJISA) 5(12), 72–80 (2013)Google Scholar
  9. 9.
    Goyal, K., Kaur, D.: A novel vehicle classification model for urban traffic surveillance using the deep neural network model. Int. J. Educ. Manag. Eng. (IJEME) 6(1), 18–31 (2016)CrossRefGoogle Scholar
  10. 10.
    Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data, Models and Simulation. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Babicheva, T.S.: The use of queuing theory at research and optimization of traffic on the signal-controlled road intersections. Proc. Comput. Sci. 55, 469–478 (2015)CrossRefGoogle Scholar
  12. 12.
    Glukharev, K.K., Ulyukov, N.M., Valuev, A.M., Kalinin, I.N.: On traffic flow on the arterial network model. In: Kozlov, V.V., et al. (eds.) Traffic and Granular Flow 2011, pp. 399–412. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Yashina, M.V., Provorov, A.V.: Verification of infocommunication system components for modeling and control of saturated traffic in Megalopolis. In: Zamojski, W., Kacprzyk, J., et al. (eds.) New Results in Dependability and Computer Systems. Advances in Intelligent Systems and Computing, vol. 224, pp. 531–542. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Valuev, A.M.: Modeling of the transport flow through crossroads with merging and divergence points. In: Proceedings of 2018 Eleventh International Conference Management of Large-Scale System Development (MLSD). Moscow, Russia, 1–3 October 2018. In: Tsvirkun, A. (ed.) IEEE Xplore Digital Library, pp. 1–3 (2018)Google Scholar
  15. 15.
    Solovyev, A.A., Valuev, A.M.: Organization of traffic flows simulation aimed at establishment of integral characteristics of their dynamics. Adv. Syst. Sci. Appl. 18(2), 1–10 (2018)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Mechanical Engineering Research Institute of the Russian Academy of SciencesMoscowRussia

Personalised recommendations