Advertisement

Wireless Networks

, Volume 25, Issue 4, pp 1505–1518 | Cite as

Movement prediction models for vehicular networks: an empirical analysis

  • Noura AljeriEmail author
  • Azzedine Boukerche
Article
  • 128 Downloads

Abstract

In recent years, the role of vehicular networks has become increasingly important for the future of Intelligent Transportation Systems, as they are useful for providing safety, assistance to drivers, and traffic control management. Many vehicular network applications such as routing, mobility management, service discovery, and collision avoidance protocols would benefit from possessing vehicles’ prior location information to improve their performance. However, the rapid mobility of vehicles and the degree of error in positioning systems create a challenging problem regarding the accuracy and efficiency of any location prediction-based model for vehicular networks. Therefore, a number of location prediction techniques has been proposed in the literature. In this paper, we study and compare the accuracy and effectiveness of the following location-based movement prediction models: Kalman filter, Extended Kalman filter (EKF), Unscented Kalman filter (UKF), and Particle filter for vehicular networks. We compare the performances of these prediction techniques with respect to different mobility models, and provide some insights on their capabilities and limitations. Our results indicate that Particle filter outperforms all other predictors with respect to location error. In addition, EKF and UKF demonstrated an increase in efficiency of more than 50% when additional measurements input were integrated with the predictors.

Keywords

Vehicular networks Prediction Kalman filter Particle filter 

References

  1. 1.
    Boukerche, A., Oliveira, H., Nakamura, E., & Loureiro, A. (2008). Vehicular ad hoc networks: a new challenge for localization-based systems. Computer Communications, 31, 28382849.Google Scholar
  2. 2.
    Tong Liu, P., Bahl, P., & Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16(6), 922–936.CrossRefGoogle Scholar
  3. 3.
    Boukerche, A. (2008). Algorithms and protocols for wireless sensor networks. Hoboken: Wiley-IEEE Press.CrossRefGoogle Scholar
  4. 4.
    Krakiwsky, E. J., Harris, C. B., & Wong, R. V. C. (1988). A Kalman filter for integrating dead reckoning, map matching and GPS positioning. Position location and navigation symposium, record. navigation into the 21st century. IEEE PLANS ’88. IEEE, 1988, pp. 39–46.Google Scholar
  5. 5.
    Parkinson, B., & Spilker, J. (1996). Global positioning system: Theory and applications. Reston: AIAA.CrossRefGoogle Scholar
  6. 6.
    Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME Journal of Basic Engineering (Series D), 82, 35–45.CrossRefGoogle Scholar
  7. 7.
    Julier, S. J., Uhlmann, J. K. (1997). New extension of the Kalman filter to nonlinear systems. In Proceedings of the SPIE 3068, signal processing, sensor fusion, and target recognition VI, Vol. 182.Google Scholar
  8. 8.
    Julier, S. J., & Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedigs of the IEEE, 92(3), 401–422.CrossRefGoogle Scholar
  9. 9.
    Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). ”Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings of the F-Radar and Signal Processing, 140(2), 107–113.CrossRefGoogle Scholar
  10. 10.
    Chen, Z. (2003). Bayesian filtering: from Kalman filters to particle filters, and beyond. Statistics, 182(1), 1–69.CrossRefGoogle Scholar
  11. 11.
    Chellapa, R., Jennings, A., & Shenoy, N. (2003). A comparative study of mobility prediction in fixed wireless networks and mobile ad hoc networks. In IEEE international conference on communications, 2003. ICC ’03, (Vol. 2, pp. 891–895).Google Scholar
  12. 12.
    Magnano, A., Fei, X., & Boukerche, A. (2015). Predictive mobile IP handover for vehicular networks. IEEE 40th conference on local computer networks (LCN) , pp. 338–346.Google Scholar
  13. 13.
    Ueki, J., Mori, J., Nakamura, Y., Horii, Y., Okada, H. (2004). Development of vehicular-collision avoidance support system by inter-vehicle communications. In Vehicular technology conference, VTC 2004-Spring, IEEE 59th (Vol. 5, pp. 2940–2945).Google Scholar
  14. 14.
    Fox, D., Hightower, J., Liao, L., Schulz, D., & Borriello, G. (2003). Bayesian filtering for location estimation. IEEE Pervasive Computing, 2(3), 24–33.CrossRefGoogle Scholar
  15. 15.
    Harri, J., & Bonnet, C., & Filali, F. (2007). The challenges of predicting mobility. Research report RR-06-171.Google Scholar
  16. 16.
    Jia, X., Wu, Z. L., Guan, H. (2016). The target vehicle movement state estimation method with radar based on Kalman filtering algorithm. In Applied mechanics and materials, 2013, (Vols. 347–350, pp. 638–642). International Journal of Distributed Sensor Networks, Vol. 12, Issue 2.Google Scholar
  17. 17.
    Feng, H., Liu, C., Shu, Y., & Yang, O. W. (2015). Location prediction of vehicles in VANETs using a Kalman filter. Wireless Personal Communications, 80(2), 543–559.CrossRefGoogle Scholar
  18. 18.
    Magnano, A., Fei, X., & Boukerche, A. (2015). Movement prediction in vehicular networks. IEEE Global Communications Conference (GLOBECOM), 2015, 1–6.Google Scholar
  19. 19.
    Namboodiri, V., & Gao, L. (2007). Prediction-based routing for vehicular Ad Hoc networks. IEEE Transactions on Vehicular Technology, 56(4), 2332–2345.CrossRefGoogle Scholar
  20. 20.
    Tzvetkov, V. (2008). SIP registration optimization in mobile environments using extended Kalman filter. In Third International Conference on Communications and Networking, 2008, pp. 106–111.Google Scholar
  21. 21.
    Vosselman, G., Knecht, J. de. (1995). Road tracing by profile matching and Kalman filtering. In A. Gruen (Ed.), Automatic extraction of man-made objects from aerial and space images, pp 265–274.Google Scholar
  22. 22.
    Merah, A. F., Samarah, S., & Boukerche, A. (2012). Vehicular movement patterns: A prediction-based route discovery technique for VANETs. IEEE international conference on communications (ICC), 2012, pp. 5291–5295.Google Scholar
  23. 23.
    Jia, X., Wu, Z. L., & Guan, H. (2013). The Target vehicle movement state estimation method with radar based on Kalman filtering algorithm. Applied Mechanics and Materials, 347–350, 638–642.CrossRefGoogle Scholar
  24. 24.
    Pascale, A., Nicoli, M., & Spagnolini, U. (2014). Cooperative Bayesian estimation of vehicular traffic in large-scale networks. IEEE Transactions on Intelligent Transportation Systems, 15(5), 2074–2088.CrossRefGoogle Scholar
  25. 25.
    Jeung, H., Yiu, M. L., Zhou, X., & Jensen, C. S. (2010). Path prediction and predictive range querying in road network databases. The VLDB Journal, 19(4), 585–602.CrossRefGoogle Scholar
  26. 26.
    Bavdekar, V. A., Deshpande, A. P., & Patwardhan, S. C. (2011). Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter. Journal of Process Control, 21(4), 585601.CrossRefGoogle Scholar
  27. 27.
    Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the kalman filter to nonlinear systems. In: Proceedigs of AeroSense: The 11th international symposium on aerospace/defence sensing, simulation and controls. Google Scholar
  28. 28.
    Srkk, S. (2013). Bayesian filtering and smoothing. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  29. 29.
    Pitt, M., & Shephard, N. (1999). Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association, 94(446), 590–599.MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Liu, J., & Chen, R. (1995). Blind deconvolution via sequential imputation. American Statistical Association, 90, 567–76.MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian non- linear state space models. Journal of Computational and Graphical Statistics, 5(1), 125.Google Scholar
  32. 32.
    Liuand, J. S., & Chen, R. (1998). Sequential Monte Carlo methods for dynamical systems . Journal of the American Statistical Association, 93, 1032–44.MathSciNetCrossRefGoogle Scholar
  33. 33.
    Vehicular mobility trace of the city of Cologne, Germany. http://kolntrace.project.citi-lab.fr/.

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

Personalised recommendations