Skip to main content
Log in

A time-triggered object tracking subsystem for advanced driver assistance systems

Zeitgesteuerte Objekterkennung in Fahrerassistenzsystemen

  • Originalarbeiten
  • Published:
e & i Elektrotechnik und Informationstechnik Aims and scope Submit manuscript

Zusammenfassung

Im Automobil der Zukunft spielen Fahrerassistenzsysteme eine wichtige Rolle. Ein wichtiges Untersystem sind dabei Objektverfolgungssysteme, welche andere Fahrzeuge mit mehreren Sensoren erfassen und deren Position berechnen. Die Architektur der derzeitigen Systeme kann jedoch oft weder Echtzeiteigenschaften noch Determinismus oder synchronisierte Verarbeitung garantieren. Um dieses Problem zu lösen, schlagen die Autoren einen Pradigmenwechsel zu einer zeitgesteuerten Architektur vor. Ein simulationsgestützter Vergleich verschiedener Ansätze legte die Vermutung nahe, dass die eventgesteuerten Modelle in Szenarien mit niedriger Dynamik bessere Ergebnisse liefern, in potentiell gefährlichen Szenarien mit hoher Dynamik aber das zeitgesteuerte Modell von Vorteil ist. Um die Realitätsnähe der Simulationsergebnisse zu überprüfen, wurden beide Ansätze in einer Testumgebung mit einem Volkswagen Touran evaluiert. Das Testfahrzeug war hierfür mit einem Laser-Scanner, einem Stereo-Kamera-System, einem FlexRay-Kommunikationssystem, einem Objektverfolgungssystem und einem Differential-GPS-System als Referenz ausgestattet.

Summary

Multi-sensor object tracking is an important feature for advanced driver assistance systems in future automobiles. Most state-of-the-art systems cannot guarantee deterministic processing of the sensor values due to unsynchronized sensing and processing units. To overcome this shortcoming we propose a paradigm shift towards a time-triggered system architecture providing a deterministic bus system, synchronized nodes, and a global time-base. The paradigm shift is supported by results of a simulation of different synchronization and scheduling approaches which suggest that although non-time-triggered approaches perform well in scenarios with low process noise, the time-triggered model becomes advantageous in potentially dangerous scenarios with high dynamics. In order to validate the results of the simulation for real life scenarios, we analyzed test drives derived from a testbed featuring a Volkswagen Touran being equipped with a laser scanner, a stereo camera system, a FlexRay communication system, an object tracking subsystem and a differential GPS system as reference.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  • Avitzour, D., Rogers, S. (1990): Optimal measurement scheduling for prediction and estimation. IEEE Transactions on Acoustics, Speech and Signal Processing 38 (10): 1733–1739

    Article  MATH  Google Scholar 

  • Elmenreich, W., Bauer, G., Kopetz, H. (2003): The time-triggered paradigm. In: Proccedings of the Workshop on Time-Triggered and Real-Time Communication Systems

  • Elmenreich, W., Pitzek, S. (2001): The time-triggered sensor fusion model. In: Proceedings of the 5th IEEE International Conference on Intelligent Engineering Systems (INES), pp. 297–300

  • Fle. (2005): FlexRay Communications System Protocol Specification Version 2.1. Available at http://www.flexray.com

  • Fox, M., (1994): Intelligent Scheduling. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Hartwich, F., Müller, B., Führer, T., Hugel, R. (2000): Time triggered communication on CAN. In: Proceedings 7th International CAN Conference, Amsterdam, The Nederlands

  • Koplin, M. (2009): Time-Triggered Object Tracking for Advanced Driver Assistance Systems, PhD thesis, Technische Universität Wien, Institut für Technische Informatik, Vienna, Austria

  • Koplin, M., Elmenreich, W. (2008): Analysis of Kalman filter based approaches for fusing out-of-sequence measurements corrupted by systematic errors. In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI'08), pp. 175–180

  • Li, Y., Krakow, L., Chong, E., Groom, K. (2006): Dynamic sensor management for multisensor multitarget tracking. In: Proc. 40th Annual Conference on Information Sciences and Systems, pp. 1397–1402

  • Mauthner, M., Altendorfer, R., Elmenreich, W., Kirchner, A. (2007): Optimization of sensor, bus, and fusion schedules of a time-triggered sensor fusion system. In: Proceedings of the 2007 IEEE Intelligent Vehicles Symposium (IV), Istanbul, Turkey, pp. 570–575

  • Mauthner, M., Elmenreich, W., Kirchner, A. (2007): Analysis of sensor and fusion schedules of a time-triggered sensor fusion system. In: Proceedings of the 10th International Conference on Information Fusion (FUSION), Quebec, Canada, pp. 1–5

  • Mehra, R. (1976): Optimization of measurement schedules and sensor designs for linear dynamic systems. IEEE Transactions on Automatic Control 21 (1): 55–64

    Article  MathSciNet  MATH  Google Scholar 

  • Mourikis, A., Roumeliotis, S. (2006): Optimal sensor scheduling for resource-constrained localization of mobile robot formations. IEEE Transactions on Robotics 22 (5): 917–931

    Article  Google Scholar 

  • Schrage, D., Gonsalves, P. (2003): Sensor scheduling using ant colony optimization. In: Proc. Sixth International Conference of Information Fusion, Vol. 1, Cairns, pp. 379–385

  • Spall, J. (2008): Improved methods for monte carlo estimation of the fisher information matrix. In: Proc. American Control Conference, pp. 2395–2400

  • Stromberg, D., Andersson, M. Lantz, F. (2002): On platform-based sensor management. In: Proc. Fifth International Conference on Information Fusion, Vol. 1, Annapolis, pp. 600–607

  • Suranthiran, S., Jayasuriya, S. (2004): Optimal fusion of multiple nonlinear sensor data. IEEE Sensors Journal 4 (5): 651–663

    Article  Google Scholar 

  • van Norden, W., de Jong, J., Bolderheij, F., Rothkrantz, L. (2005): Intelligent task scheduling in sensor networks. In: Proc. 8th International Conference on Information Fusion, Vol. 2, Philadelphia

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Elmenreich, W., Koplin, M. A time-triggered object tracking subsystem for advanced driver assistance systems. Elektrotech. Inftech. 128, 203–208 (2011). https://doi.org/10.1007/s00502-011-0004-x

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00502-011-0004-x

Schlüsselwörter

Keywords

Navigation