RFID Data-Driven Vehicle Speed Prediction Using Adaptive Kalman Filter

  • Anqi FengEmail author
  • Liping Qian
  • Yupin Huang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


This paper focuses on the design of radio frequency identification (RFID) data-driven vehicle speed prediction method using adaptive Kalman filtering. First of all, when the vehicle moves through a RFID tag, the reader needs to acquire the state information (i.e., current speed and time stamp) of the last vehicle across the tag, and meanwhile transmits its state information to this tag. Then, the state space model can be formulated according to the acquired state information. Finally, the adaptive Kalman filtering algorithm is proposed to predict and adjust the speed of vehicles. Adaptive Kalman filtering algorithm achieves the adaptive updating of variable forgetting factor by analyzing the error between the expected output value and the actual output value, so as to achieve the online updating of the prediction model. The numerical results further show that compared with the conventional Kalman filtering algorithm, the proposed algorithm can increase the speed prediction accuracy by 20%. This implies that the proposed algorithm can provide the better real-time effectiveness for the practical implementation.


Speed prediction RFID Data acquisition Adaptive Kalman filter 



This work was supported in part by the National Natural Science Foundation of China under Project 61379122, Project 61572440, and Project 61502428, and in part by the Zhejiang Provincial Natural Science Foundation of China under Project LR16F010003, and Project LR17F010002.


  1. 1.
    Yang, M., Dong, B., Wang, H., et al.: Laser radar based real time ego-motion estimation for intelligent vehicles. In: IEEE Intelligent Vehicle Symposium, vol. 1, pp. 44–51 (2002)Google Scholar
  2. 2.
    Floudas, N., Polychronopoulos, A., Amditis, A.: A survey of filtering techniques for vehicle tracking by radar equipped automotive platforms. In: International Conference on Information Fusion, vol. 2, pp. 25–28 (2005)Google Scholar
  3. 3.
    Zamiri, S., Reitinger, B., Grun, H., et al.: Laser ultrasonic velocity measurement for phase transformation investigation in titanium alloy. In: IEEE International Ultrasonics Symposium, pp. 683–686 (2013)Google Scholar
  4. 4.
    Titov, S.A., Maev, R.G., Bogachenkov, A.N.: An ultrasonic array technique for velocity of bulk waves and sample thickness measurement. In: IEEE International Ultrasonics Symposium, pp. 2384–2387 (2010)Google Scholar
  5. 5.
    Kalashnikov, A.N., Challis, R.E.: Errors and uncertainties in the measurement of ultrasonic wave attenuation and phase velocity. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 52, 1754–1768 (2005)CrossRefGoogle Scholar
  6. 6.
    Shen, Q., Ban, X.-J., Chang, Z., et al.: On-line detection and temporal segmentation of actions in vidio based human-computer interaction. Chin. J. Comput. 38(12), 2477–2487 (2015)Google Scholar
  7. 7.
    Kloos, G., Guivant, J.E., Worrall, S., et al.: Wireless network for mining applications. In: Australasian Conference on Robotics and Automation, Canberra, Australia, December 2004Google Scholar
  8. 8.
    Yoon, J.-H., Peng, H.: A cost-effective sideslip estimation method using velocity measurements from two GPS receivers. IEEE Trans. Veh. Technol. 63, 2589–2599 (2014)CrossRefGoogle Scholar
  9. 9.
    Bhavsar, S.S., Kulkarni, A.N.: Train collision avoidance system by using RFID. In: International Conference on Computing, Analytics and Security Trends, pp. 30–34 (2016)Google Scholar
  10. 10.
    Li, J., Jin, M., Luan, S.: Intriduction radio frequency identification technology. Comput. Knowl. Technol. 6(15), 4238–4240 (2010)Google Scholar
  11. 11.
    Liu, W., Ning, H., Wang, B.: REID antenna design of highway ETC in ITS. In: International Symposium on Antennas, Propagation and EM Theory, pp. 1–4 (2006)Google Scholar
  12. 12.
    Guo, Y., Zhao, Z.: Design of school bus passengers’ identity authentication system based on RFID. In: IEEE International Conference on Communication Problem-Solving, pp. 412–415 (2015)Google Scholar
  13. 13.
    Lee, L.T., Tsang, K.F.: An active RFID system for railway vehicle identification and positioning. In: International Conference on Railway Engineering - Challenges for Railway Transportation in Information Age, pp. 1–4 (2008)Google Scholar
  14. 14.
    Huo, Y., Lu, Y., Cheng, W., et al.: Vehicle road distance measurement and maintenance in RFID systems on roads. In: 2014 International Conference on Connected Vehicles and Expo, pp. 30–36 (2014)Google Scholar
  15. 15.
    Qiong, D., Yang, X., Zhu, J.: Study on tracking algorithm for wMPS based on least square Kalman filter. Chin. J. Sens. Actuators 25(2), 236–239 (2012)Google Scholar
  16. 16.
    Jing, J., Filev, D., Kurt, A., et al.: Vehicle speed prediction using a cooperative method of fuzzy Markov model and auto-regressive model. In: 2017 IEEE Intelligent Vehicles Symposium, pp. 881–886 (2017)Google Scholar
  17. 17.
    Chen, X., Ling, Y., Chen, M.: Mobile robot localization algorithm based on gaussian mixture consider Kalman filter in WSNs environment. Chin. J. Sens. Actuators 30(1), 133–138 (2017)Google Scholar
  18. 18.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, vol. 8, no. 7, pp. 127–132 (2006)Google Scholar
  19. 19.
    Sun, Y., Zhang, C.: Research on the detection method of airport pavement joint seeper based on Kalman filter. Chin. J. Sens. Actuators 30(8), 1204–1208 (2017)Google Scholar
  20. 20.
    Heidari, A., Khandani, A.K., Mcavoy, D.: Adaptive modelling and long-range prediction of mobile fading channels. IET Commun. 4, 39–50 (2010)CrossRefGoogle Scholar
  21. 21.
    Cai, X., Cai, M., Zhang, Y.: Research on driver reaction time in internet of vehicles environment. J. Comput. Appl. 37(S2), 270–273 (2017)MathSciNetGoogle Scholar
  22. 22.
    Yang, L., Xing, C., Zhao, H.: Study on driver’s reaction time (DRT) during car following. Comput. Technol. Autom. 34(3), 33–37 (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.College of Information EngineeringZhejiang University of TechnologyHangzhouChina

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