FUZZY ESTIMATION OF VEHICLE SPEED USING AN ACCELEROMETER AND WHEEL SENSORS

  • HWANG J. K. (Department of Electrical Engineering, Woosuk University) ;
  • SONG C. K. (School of Mechanical and Aerospace Engineering, Engineering Research Institute (ERI), Gyeongsang National University)
  • Published : 2005.06.01

Abstract

The absolute longitudinal speed of a vehicle is estimated by using data from an accelerometer of the vehicle and wheel speed sensors of a standard 50-tooth antilock braking system. An intuitive solution to this problem is, 'When wheel slip is low, calculate the vehicle velocity from the wheel speeds; when wheel slip is high, calculate the vehicle speed by integrating signal of the accelerometer.' The speed estimator weighted with fuzzy logic is introduced to implement the above concept, which is formulated as an estimation method. And the method is improved through experiments by how to calculate speed from acceleration signal and slip ratios. It is verified experimentally to usefulness of estimation speed of a vehicle. And the experimental result shows that the estimated vehicle longitudinal speed has only a $6\%$ worst-case error during a hard braking maneuver lasting a few seconds.

Keywords

References

  1. Bakker, E., Pacejka, H. and Lidner, L. (1989). A new tire model with an application in vehicle dynamics studies. SAE Trans.J of Passenger Cars 98, 101-113
  2. Basset, M., Zimmer, C. and Gissinger, G.L. (1997). Fuzzy approach to the real time longitudinal velocity estimation of a FWD car in critical situations. Vehicle System Dynamics 27, 5-6, 477-489
  3. Bevly, D.M., Sheridan, R. and Gerdes, J.C. (2001). Integrating INS sensors with GPS velocity measurements for continuous estimation of vehicle sideslip and tire cornering stiffness. Proc. American Control Conference, Arlington, VA, USA, 25-30, June 25-27
  4. Daiss, A. and Kiencke, U. (1995). Estimation of vehicle speed fuzzy-estimation in comparison with Kalmanfiltering. Proc. of the 4th IEEE Conference on Control Applications, Albany, NY, USA, 281-284
  5. Ivanov, V., Belous, M., Liakhau, S. and Miranovich, D. (2005). Results of Functional Simulation for ABS with Pre-Extreme Control. Int. J. Automotive Technology 6, 1, 37-44
  6. Kiencke, U. and Daiss, A. (1994).Estimation of tyre friction for enhanced ABS-systems. Proceedings of AVEC '94, Int. Symposium on Advanced Vehicle Control, Tsukuba, Japan, 515-520
  7. Kobayashi, K., Cheok, Ka C.and Watanabe, K. (1995). Estimation of absolute vehicle speed using fuzzy logic rule-based Kalman filter.Proc.American Control Conference, Seattle, Washington, 3086-3090, June
  8. Miller, S.L., Youngberg, B., Millie, A., Schweitzer, P. and Gerdes, J.C. (2001). Calculating longitudinal wheel slip and tire parameters using GPS velocity, Proc. American Control Conference, Arlington, VA, USA, 1800-1805, June 25-27
  9. Oh, K. O. and Song, C. K. (2002). Absolute vehicle speed estimation using neural network model. Trans. of KSPE 19, 9, 51-58
  10. Pasterkamp, W. R. and Pacejka, H. B. (1996). The tyre as a sensor to estimate friction, Proc.of AVEC '96, Int.Symposium on Advanced Vehicle Control, Monterey, CA, USA, 839-853
  11. Song, C. K., Hwang, J. K.and Hedrick, J. K. (2002). Absolute vehicle speed estimation using fuzzy logic. Trans. Korean Society of Automotive Engineers 10, 1, 179-186
  12. Song, C. K., Uchanski, M.and Hedrick, J. K. (2002). Vehicle speed estimation using accelerometer and wheel speed measurements, ATT Congress, Paris, France 2002-01-2229