Encyclopedia of Sustainability Science and Technology

2012 Edition
| Editors: Robert A. Meyers

Unscented Kalman Filter in Intelligent Vehicles

  • Moustapha DoumiatiEmail author
  • Daniel Lechner
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-0851-3_791

Definition of the Subject

The principal concerns in driving safety with standard vehicles are understanding and preventing risky situations. A close examination of accident data reveals that losing the vehicle control is the main reason for most car accidents. To help the driver to prevent such accidents, vehicle control systems may be used. For their optimal operation, these control systems require certain input data concerning vehicle dynamic parameters and vehicle–road interaction . Unfortunately, some fundamental parameters like the tire-road forces and the sideslip angle are difficult to measure in a car, for both technical and economic reasons. To face this problem, this study presents a dynamic modeling and observation method to estimate these variables. The ability to accurately estimate lateral tire forces and sideslip angle is a critical determinant in the performances of many vehicle control systems. To address nonlinearities and unmodeled vehicle dynamics, an...

This is a preview of subscription content, log in to check access.



This work was done at Heudiasyc Laboratory UMR CNRS 6599, UTC University (Compiègne, France) in collaboration with Alessandro Victorino and Ali Charara.


Primary Literature

  1. 1.
    FARS (2007) Fatality analysis reporting system (FARS). Technical report, National Highway Traffic Safety Administration, National Center for Statistics and AnalysisGoogle Scholar
  2. 2.
    Aparicio F, Paez P, Moreno F, Jimenez F, Lopez A (2005) Discussion of a new adaptive speed control system incorporating the geometric characteristics of the road. Int J Veh Auton Syst 3(1):47–64CrossRefGoogle Scholar
  3. 3.
    Bosch R (2004) Bosch automotive handbook, 6th edn. SAE Society of Automotive Engineers, Bosch company, New YorkGoogle Scholar
  4. 4.
    ESC (2003) Electronic stability control fact sheet. Technical report, ESC Coalition and DEKRA Automotive researchGoogle Scholar
  5. 5.
    Van Zanten A (2002) Evolution of electronic control systems for improving the vehicle dynamic behavior. In: Proceedings of the 6th international symposium on advanced vehicle control (AVEC), Hiroshima, pp 7–15Google Scholar
  6. 6.
    Hsu Y, Gerdes JC (2005) Stabilization of a steerby-wire vehicle at the limits of handling using feedback linearization. In: Procedings of IMECE2005, AMSE international mechanical engineering congress and exposition, FloridaGoogle Scholar
  7. 7.
    Lechner D (2008) Embedded laboratory for vehicle dynamic measurements. In: International symposium on advanced vehicle control, KobeGoogle Scholar
  8. 8.
    Shim T, Ghike G (2007) Understanding the limitations of different vehicle models for roll dynamics studies. Veh Syst Dyn 45:191–216CrossRefGoogle Scholar
  9. 9.
    Doumiati M, Baffet G, Lechner D, Victorino A, Charara A (2008) Embedded estimation of the tire/road forces and validation in a laboratory vehicle. In: International symposium on Advanced Vehicle Control, KobeGoogle Scholar
  10. 10.
    Doumiati M, Victorino A, Charara A, Lechner D, Baffet G (2008) An estimation process for vehicle wheelground contact normal forces. In: IFAC World Congress, Seoul KoreaGoogle Scholar
  11. 11.
    Doumiati M, Victorino A, Charara A, Lechner D (2009) Lateral load transfer and normal forces estimation for vehicle safety experimental evaluation. Veh Syst Dyn 47(12):1511–1533CrossRefGoogle Scholar
  12. 12.
    Wenzel TA, Burnham KJ, Blundell MV, Williams RA (2006) Estimation of the nonlinear suspension tyre cornering forces from experimental road test data. Veh Syst Dyn 44:153–171CrossRefGoogle Scholar
  13. 13.
    Dakhlallah J, Glaser S, Mammar S, Sebsadji Y (2008) Tire-road forces estimation using extended Kalman filter and sideslip angle evaluation. In: American control conference, SeattleGoogle Scholar
  14. 14.
    Ray LR (1997) Nonlinear tire force estimation and road friction identification: simulation and experiments. Automatica 33(10):1819–1833CrossRefGoogle Scholar
  15. 15.
    Baffet G, Charara A, Dherbomez G (2007) An observer of tire road forces and friction for active-security vehicle systems. IEEE/ASME Trans Mechatron 12:651–661CrossRefGoogle Scholar
  16. 16.
    Baffet G, Charara A, Lechner D, Thomas D (2008) Experimental evaluation of observers for tire-road forces, sideslip angle and wheel cornering stiffness. Veh Syst Dyn 45:191–216Google Scholar
  17. 17.
    Wilkin MA, Manning WJ, Crolla DA, Levesley MC (2006) Use of an extended Kalman filter as a robust tyre force estimator. Veh Syst Dyn 44:50–59CrossRefGoogle Scholar
  18. 18.
    Venhovens PT, Naab K (1999) Vehicle dynamics estimation using Kalman filters. Veh Syst Dyn 32:171–184CrossRefGoogle Scholar
  19. 19.
    Kiencke U, Daiss A (1997) Observation of lateral vehicle dynamics. Control Eng Pract 5(8):1145–1150CrossRefGoogle Scholar
  20. 20.
    Hsu YHJ (2009) Estimation and control of lateral tire forces using steering torque. PhD, Department of mechanical engineering, Stanford UniversityGoogle Scholar
  21. 21.
    Haffner L, Kozek M, Shi J, Jorgel HP (2008) Estimation of the maximum friction coefficient for a passenger vehicle using the instantaneous cornering stiffness. In: American control conference, SeattleGoogle Scholar
  22. 22.
    Gustafsson F (1997) Slip-based tire-road friction estimation. Automatica 33(6):1087–1099CrossRefGoogle Scholar
  23. 23.
    Hahn JO, Rajamani R, Alexander L (2002) GPS-Based real time identification of tire-road friction coefficient. IEEE Trans Control Syst Technol 10(3):331–343CrossRefGoogle Scholar
  24. 24.
    Uchanski MR (2001) Road friction estimation for automobiles using digital signal processing methods. PhD thesis, University of California, BerkleyGoogle Scholar
  25. 25.
    Koo SL, Tan HS (2007) Tire dynamic deflection and its impact on vehicle longitudinal dynamics and control. IEEE/ASME Trans Mechatron 12(6):623–631CrossRefGoogle Scholar
  26. 26.
    Dugoff J, Fanches P, Segel L (1970) An analysis of tire properties and their influence on vehicle dynamic performance. Society of Automotive Engineers Paper 700377CrossRefGoogle Scholar
  27. 27.
    Pacejka HB (2002) Tyre and vehicle dynamics. Elsevier, AmsterdamGoogle Scholar
  28. 28.
    Gillespie TD (1992) Fundamental of vehicle dynamics. Society of Automotive Engineers, WarrendaleGoogle Scholar
  29. 29.
    Rajamani R (2005) Vehicle dynamics and control. Springer, New YorkGoogle Scholar
  30. 30.
    Lidner L (1993) Experience with the magic formula tyre model. In: Proceedings of the 1st international colloquium on tyre models for vehicle dynamic analysis, AmsterdamGoogle Scholar
  31. 31.
    Svendenius J (2007) Tire modeling and friction estimation. PhD thesis, Lund University, SwedenGoogle Scholar
  32. 32.
    Nijmeijer H, Van der Schaft AJ (1991) Nonlinear dynamical control systems. Springer, New YorkGoogle Scholar
  33. 33.
    Doumiati M, Victorino A, Charara A, Lechner D (2009) Unscented Kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation. In: IEEE intelligent vehicles symposium, Xi’an, ShaanxiGoogle Scholar
  34. 34.
    Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82(D):35–45CrossRefGoogle Scholar
  35. 35.
    Welch G, Bishop G (2001) An introduction to the Kalman filter, Course 8. University of North Carolina, Departement of computer scienceGoogle Scholar
  36. 36.
    Durrant-Whyte H (2001) Multi sensor data fusion. Technical report, Australian center for field robotics, University of SydneyGoogle Scholar
  37. 37.
    Mohinder GS, Angus PA (1993) Kalman filtering theory and practice. Prentice Hall, Englewood CliffsGoogle Scholar
  38. 38.
    Julier SJ, Uhlman JK (1997) A new extension of the Kalman filter to nonlinear systems. In: International symposium aerospace/defense sensing, simulation and controls, OrlandoGoogle Scholar
  39. 39.
    Julier SJ, Uhlman JK, Durrant-Whyte HF (1995) A new extension approach for filtering nonlinear systems. In: American control conference, SeattleGoogle Scholar
  40. 40.
    Wan EA, Merwe RVD (2000) The unscented Kalman filter for nonlinear estimation. In: Proceedings of the adaptive systems for signal processing, communications, and control symposium, Atlanta, pp 153–158Google Scholar
  41. 41.
    Milliken W, Milliken DL (1995) Race car vehicle dynamics. Society of Automotive Engineers, WarrendaleGoogle Scholar

Books and Reviews

  1. Aga M, Okada A (2003) Analysis of vehicle stability control (VSC)’s effectiveness from accident data. In: Proceedings of the 18th international technical conference on the enhanced safety of vehicles, NagoyaGoogle Scholar
  2. Aguirre LA, Letellier C (2005) Observability of multivariate differential embeddings. J Phys A Math Gen 38:6311–6326CrossRefGoogle Scholar
  3. Bakker E, PAcejka HB, Lidner L (1987) A new tire model with application in vehicle dynamic systems, SAE Paper 890087Google Scholar
  4. Besanon G (2007) Nonlinear observers and applications. Springer, BerlinGoogle Scholar
  5. Bevly DM, Gerdes JC, Wilson C, Zheng G (2000) The use of GPS based velocity measurements for improved vehicle state estimation: proceedings of the American Control Conference, Chicago. American Automatic Control Council, New YorkGoogle Scholar
  6. Brown T, Hac A, Martens J (2004) Detection of vehicle rollover. SAE World Congress, DetroitGoogle Scholar
  7. Doumiati M, Sename O, Martinez J, Poussot C, Dugard L (2010) Gain scheduled steering and braking control for improvement of vehicle handling and stability. In: IEEE conference on decision and control, GeorgiaGoogle Scholar
  8. Doumiati M, Sename O, Martinez J, Dugard L, Gaspar P, Szabo Z, Bokor J (2011) Vehicle yaw control via coordinated use of steering/braking systems. IFAC World Congress, MilanoGoogle Scholar
  9. Heydinger G, Howe J (2000) Analysis of vehicle response data measured during severe maneuvers, SAE Paper 2000-01-1644CrossRefGoogle Scholar
  10. Jazar RN (2008) Vehicle dynamics, theory and application. Springer, New YorkCrossRefGoogle Scholar
  11. Smith ND (2004) Understanding parameters influencing tire modeling. Colorado State University, Formula SAE PlatformGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.B2i Automotive Engineering CompanyMassyFrance
  2. 2.Department of Accident Mechanism AnalysisIFSTTAR-MA LaboratorySalon de ProvenceFrance