Advertisement

Design of the State Estimation System in the Advanced Driver Assistance System

  • Wei HuangEmail author
  • Xiaoxin Su
  • David Bevly
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 452)

Abstract

The development of Advanced Driver Assistance System (ADAS) is emphasized in road transportation research. Reliability and safety is one of the most important issues in the design process of ADAS. In general, the system must fuse information from multiple sensors to obtain more complete and accurate information about the world. In this chapter, a novel loose coupling sensor fusion strategy is designed, which uses the Extended Kalman filtering (EKF) to fuse the sensor measurements from odometers, accelerometers, gyroscope, and GPS. By using a novel four-wheel vehicle model, the EKF is able to conduct a multi-output rate sensor fusion, compensate the latency for GPS signals, and increase the accuracy of vehicle state estimation even if there exist sensor errors, such as GPS outage, odometer reading error due to wheel slippage. From the road test, it is proved that the designed EKF has achieved good results for vehicle state estimation.

Keywords

Global Position System Extend Kalman Filter Wheel Speed Controller Area Network Differential Global Position System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Becker JC, Simon A (2000) Sensor and navigation data fusion for an autonomous vehicle. In: Proceedings of the IEEE intelligent vehicle symposiumGoogle Scholar
  2. 2.
    Ryu J, Rossetter EJ, Gerdes JC (2002) Vehicle sideslip and roll parameter estimation using GPS. In: Proceedings of AVEC 2002, Japan, Sept 9–13 2002Google Scholar
  3. 3.
    Fukuba H, Adachi T, Yoshimoto A, Takahashi H, Yoshioka T (2002) Precise 6-DOF movement measurement for vehicle by GPS and angular rate sensor and Kalman filter. In: Proceedings of AVEC 2002, Japan, 9–13 Sept 2002Google Scholar
  4. 4.
    Carlson CR, Gerdes JC, Powell, JD (2002) Practical position and yaw rate estimation with GPS and differential wheelspeeds. In: Proceedings of AVEC 2002, Japan, 9–13 Sept 2002Google Scholar
  5. 5.
    Gustafson F, Persson N, Forsell U, Ahlqvist S (2001) Sensor fusion and accurate computation of yaw rate and absolute velocity. In: SAE 2001 World Congress Detroit, Michigan, 5–8 March 2001Google Scholar
  6. 6.
    Healy AJ, An EP, Marco DB (1998) On line compensation of heading sensor bias for low cost AUVs. In: Proceedings of the IEEE workshop on autonomous underwater vehicles, AUV98, Cambridge, Mass, pp 35–42, Aug 20–21, 1998Google Scholar
  7. 7.
    Kiriy E, Buehler M (2002) Three-state extended Kalman filter for mobile robot localization. Centre for Intelligent Machines (CIM), McGill University, April 12 2002Google Scholar
  8. 8.
    Qu S, Tian Y, Chen C, Ai L (2012) A small intelligent car system based on fuzzy control and CCD camera. Int J Model Ident Control 15(1):48–54Google Scholar
  9. 9.
    Ren W, Gu Q, He D, Zhao J (2013) Modelling and implementation of a car-like mobile robot for trajectory-tracking. Int J Model Ident Control 19(2):150–160Google Scholar
  10. 10.
    Shamsudin SS, Chen X (2012) Identification of an unmanned helicopter system using optimised neural network structure. Int J Model Ident Control 17(3):223–241Google Scholar
  11. 11.
    Thrapp R, Westbrook C, Subramanian D (2001) Robust localization algorithms for an autonomous campus tour guide. In: Proceedings of the 2001 IEEE international conference on robotics and automation, vol 2, pp 2065–2071Google Scholar
  12. 12.
    Grewal M, Andrews A (2000) Kalman filtering theory and practice using matlab, 2nd edn. Wiley, New YorkGoogle Scholar
  13. 13.
    Lee D, Tomizuka M (2003) Multirate optimal state estimation with sensor fusion. In: American control conference, Denver, June 4–6 2003Google Scholar
  14. 14.
    Bouvet D, Garcia G (2000) Improving the accuracy of dynamic localization systems using RTK GPS by identifying the GPS latency. In: Proceedings of the IEEE international conference on robotics and automation, San Francisco, pp 2525–2530 April 2000Google Scholar
  15. 15.
    Wada M, Yoon KS, Hashimoto H (2000) High accuracy multisensor road vehicle state estimation. In: IEEE international conference on industrial electronics, control and instrumentation, Nagoya, Japan, October 2000Google Scholar
  16. 16.
    Hermann R, Krener A (1977) Nonlinear controllability and observability. IEEE Trans Autom Control 22(5):728–740CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Mechanical EngineeringAuburn UniversityAuburnUSA
  2. 2.Navistar IncLisleUSA

Personalised recommendations