A System Architecture for Heterogeneous Signal Data Fusion, Integrity Monitoring and Estimation of Signal Quality

Chapter

Abstract

A large number of today’s automobiles, right down to the compact car segment, are equipped with vehicle dynamics control and driver assistance systems. In general, each one of these functions has been developed with its own dedicated set of sensors, and applied independently of other sensors installed in the same vehicle. As a result, redundant measurements are performed, the advantages of which are currently only utilized in a few cases. The increasing powerfulness of microprocessors and the availability of bus systems in vehicles offer a basis for a central processing of the large quantity of data already available. In this article, criteria are shown for selecting and combining methods for data fusion, integrity monitoring and signal quality estimation from a system design and integration point of view, i.e. which integrity and error detection algorithm fits the requirements for working with signals from cost-effective series sensors and offering useful results for the application together with driving dynamics control and driver assistance systems. Also, the ease of series application and usability in existing system architectures will be considered.The structure of a system architecture for centralized, consistent fusion of random pieces of data is shown and evaluated using an example. The sensors used here are acceleration and yaw rate sensors produced using micro-electro-mechanical system (MEMS) technology combined to an inertial measurement unit (IMU) with 3 degrees of freedom, a single-channel (L1) GPS receiver that issues raw data (pseudo ranges and carrier phase measurement), as well as odometry sensors measuring angle pulses from all four wheels and the steering wheel angle. In addition, an evaluation of the signal quality based on usage of redundancies is shown. In general, many different, proven methods for evaluating the integrity of signals or a sensor fusion system exist. Although first definitions of, and requirements for integrity in the automotive sector exist, no appropriate algorithm concept, and no system architecture has yet been defined, and the existing methods only partly fulfill the requirements. Hence, in this article a top-down approach will be shown, beginning at the requirements of automotive integrity, and leading to the definition of an automotive integrity and accuracy benchmark, as well as a system architecture suited for the integration into existing and future system setups. For this purpose, a description of the signal quality by means of integrity and accuracy is shown, and components for such a description are shown by way of example.

Keywords

Sensor data fusion Integrity Accuracy Signal quality 

References

  1. Bar-Shalom, Y., Li, X., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation, vol. 1. Wiley, New York (2001)Google Scholar
  2. Bhatti, U.I., Ochieng, W.: Detecting multiple failures in GPS/INS integrated system: a novel architecture for integrity monitoring. J. Glob. Position. Syst. 8(1), 26–42 (2009) (Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College, London) (2009)Google Scholar
  3. Bickford, R.L., Bickmore, T.W., Caluori, V.A.: Real-Time Sensor Validation for Autonomous Flight Control. The American Institute of Aeronautics and Astronautics, Reston (1997)Google Scholar
  4. Brown, R.G.: A Baseline RAIM scheme and a note on the equivalence of three RAIM methods. In: Proceedings of the 1992 National Technical Meeting of the Institute of Navigation, San Diego, California (1992)Google Scholar
  5. Dziubek, N., Winner, H., Becker, M., Leinen, S.: Fahrstreifengenaue Ortung von Kraftfahrzeugen durch Datenfusion und Fehlerkompensation von Standard-Seriensensoren. In: DGON-Symposium Positionierung und Navigation für Intelligente Verkehrssysteme, POSNAV ITS 2011, Darmstadt (2011)Google Scholar
  6. Dziubek, N., Winner, H., Becker, M., Leinen, S.: Sensordatenfusion zur hochgenauen Ortung von Kraftfahrzeugen mit integrierter Genauigkeits- und Integritätsbewertung der Sensorsignale. In: 5. Tagung Fahrerassistenz, TÜV Süd - 5. Tagung Fahrerassistenz: Schwerpunkt Vernetzung, Munich (2012)Google Scholar
  7. Feng, S., Ochieng, W.: Integrity of navigation systems for road transport. In Proceedings 14th World Congress of Intelligent Transportation Systems, Beijing (2007)Google Scholar
  8. Goebel, K., Agogino, A.: Fuzzy sensor fusion for gas turbine power plants. In Proceedings of SPIE, Sensor Fusion: Architecture, Algorithms, and Applications III, vol. 3719, 7–9 April ’99, Orlando, Florida (1999)Google Scholar
  9. Horn, M., Dourdoumas, N.: Regelungstechnik. Pearson Verlag, Munich (2004)Google Scholar
  10. Ibargüengoytia, P.H., Sucar, L.E., Vadera,S.: Real-time intelligent sensor validation. IEEE Trans. Power Syst. 16(4), 770-775 (Atlanta, Georgia) (2001)Google Scholar
  11. Kammeyer, K.D., Kroschel, K.: Digitale Signalverarbeitung. B. G. Teubner, Stuttgart (1998)Google Scholar
  12. Kuusniemi, H.: User-Level Reliability and Quality Monitoring in Satellite-Based Personal Navigation. Tampere University of Technology, Finland, Institute of Digital and Computer Systems, Tampere (2005)Google Scholar
  13. Leinen, S.: Parameterschätzung I / Parameter estimation I. Institute of Physical Geodesy, TU Darmstadt, Darmstadt (2010)Google Scholar
  14. Le Marchand, O., Bonnifait, P., Ibañez-Guzmán, J., Bétaille, D.: Vehicle Localization Integrity Based on Trajectory Monitoring. Intelligent Robots and Systems, St. Louis, Missouri (2009)Google Scholar
  15. Liu, J., Tang, T., Gai, B., Wang, J.C.: Integrity Assurance of GNSS-Based Train Integrated Positioning System. Beijing Jiaotong University, Beijing, Science China Press and Springer-Verlag, Berlin, Heidelberg, State Key Laboratory of Rail Traffic Control and Safety (2011)Google Scholar
  16. Mansfeld, W.: Satellitenortung und Navigation, 2nd edn. Vieweg Verlag, Wiesbaden (2004)Google Scholar
  17. IS-GPS-200: Interface Specification Revision D, Space and Missile Systems Center (SMC), Navstar GPS Joint Program Office (SMC/GP). El Segundo, California (2006)Google Scholar
  18. Niebuhr, J., Lindner, G.: Physikalische Messtechnik mit Sensoren, vol. 5. Oldenbourg Industrieverlag, Munich (2002)Google Scholar
  19. Pourret, O., Naim, P., Marcot, B.: Bayesian Networks: A Practical Guide to Applications. Wiley, West Sussex (2008)Google Scholar
  20. Soika, M.: A sensor failure detection framework for autonomous mobile robots. In: Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems, Grenoble (1997)Google Scholar
  21. Toledo-Moredo, R., Zamora-Izquierdo, M.A., Úbeda-Miñarro, B.: High-Integrity IMM-EKF-Based Road Vehicle Navigation with Low-Cost GPS / SBAS / INS. IEEE Trans. Intell. Transp. Syst. 8(3), 491–511 ( Università di Parma, Parma) (2007)Google Scholar
  22. Wendel, J.: Integrierte Navigationssysteme. Sensordatenfusion, GPS und Inertiale Navigation, Oldenbourg Wissenschaftsverlag, Munich (2007)Google Scholar
  23. Young, R.S.Y., McGraw, G.A.: Fault detection and exclusion using normalized solution separation methods. In Proceedings of the 15th International Technical Meeting of the Satellite Division of the Institute of Navigation, Portland, Oregon (2002)Google Scholar
  24. Zogg, J.-M. (2009): GPS und GNSS: Grundlagen der Navigation und Ortung mit Satelliten (updated Oct. 2011), \(\upmu \)Blox AG, ThalwilGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Automotive EngineeringTechnische Universität DarmstadtDarmstadtGermany

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