Static Software Architecture of the Sensor Data Fusion Module of the Stadtpilot Project

Chapter

Abstract

Despite the advances in automatic driving in the last years, running an automatic vehicle in public traffic is still quite a challenge. One of the main components of an automatic car is the environmental perception system. It processes the measurement data of different sensors and provides the basis for the decision algorithms. As part of the project Stadtpilot at TU Braunschweig, a flexible architecture for environmental perception has been developed. This paper presents the architecture’s static view. It offers an easy to use object oriented framework for creating different sensor data fusion applications for vehicle environmental perception. By defining detailed interfaces between the architecture’s elements down to single classes, algorithms and processing stages can be easily replaced to support the developer.

Keywords

Stadtpilot Sensor data fusion Software architecture Object hypotheses based Grid based UML 

References

  1. Al-Dhaher, A., Mackesy, D.: Multi-sensor data fusion architecture. Proceedings of the 3rd IEEE International Workshop on Haptic, Audio and Visual Environments and Their Applications, HAVE, pp. 159–163. Ottawa (2004)Google Scholar
  2. Bass, L., Clements, P., Kazman, R.: Software Architecture in Practice, 2nd edn. Addison-Wesley Professional, Boston (2003)Google Scholar
  3. Bedworth, M.D., O’Brien, J.: The Omnibus model: a new model for data fusion?. Proceedings of the 2nd International Conference on Information Fusion, pp. 437–444. Fusion, Sunnyvale (1999)Google Scholar
  4. Bertsekas, D.P.: A distributed algorithm for the assignment problem, Lab. for information and decision systems report. MIT, Cambridge (1979)Google Scholar
  5. Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House Publishers, Norwood (1999)MATHGoogle Scholar
  6. Blom, H.A.P., : An efficient filter for abruptly changing systems, Proceedings of the 23rd IEEE Conference on Decision and Control, pp. 656–658. CDC, Las Vegas (1984)Google Scholar
  7. Boyd, R.R.: A Discourse on Winning and Losing: Slides Air University Library. Maxwell, AL (1976)Google Scholar
  8. Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-Oriented Software Architecture - A System of Patterns, vol. 1. Wiley, West Sussex (1996)Google Scholar
  9. Carvalho, H.S., Heinzelman, W.B.: A general aata fusion architecture. Proceedings of the 6th International Conference on Information Fusion, pp. 1465–1472. Fusion, Cairns (2003)Google Scholar
  10. Darms, M.: Eine Basis-Systemarchitektur zur Sensordatenfusion von Umfeldsensoren für Fahrerassistenzsysteme. Ph.D thesis, Technische Universität Darmstadt, Fachgebiet Fahrzeugtechnik (2007)Google Scholar
  11. Darms, M., Winner, H.: A modular system architecture for sensor data processing of ADAS applications. In: Proceedings of IEEE Intelligent Vehicles Symposium IV Las vegas, pp. 729–734 (2005)Google Scholar
  12. Dasarathy, B.: Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proc. IEEE 85(1), 24–38 (1997)Google Scholar
  13. Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Berlin (2009)MATHCrossRefGoogle Scholar
  14. Dietmayer, K.; Kirchner, A. and Kämpchen, N.: Fusionsarchitekturen zur Umfeldwahrnehmung für zukünftige Fahrerassistenzsysteme. In: Maurer, M., Stiller, C. (eds.) Fahrerassistenzsysteme mit maschineller Wahrnehmung. Springer, pp. 59–88. Berlin Heidelberg (2005)Google Scholar
  15. Durrant-Whyte, H.; Rao, B. Hu, H.: Toward a fully decentralized architecture for multi-sensor data fusion. Proceedings of IEEE International Conference on Robotics and Automation, Vol. 2 of ICRA, pp. 1331–1336. Cincinnat (1990)Google Scholar
  16. Effertz, J.: Sensor architecture and data fusion for robotic perception in Urban environments at the 2007 DARPA Urban challenge. Proceedings of the 2nd International Conference on Robot Vision, pp. 275–290. RobVis, Auckland (2008)Google Scholar
  17. Eidehall, A.; Schon, T., Gustafsson, F.: The marginalized particle filter for automotive tracking applications. In: Proceedings of IEEE Intelligent Vehicles Symposium IV Las vegas, pp. 370–375 (2005)Google Scholar
  18. Freyer, J., Deml, B., Maurer, M., Färber, B.: ACC with enhanced situation awareness to reduce behavior adaptions in lane change situations. Proceedings of the IEEE Intelligent Vehicles Symposium, IV, Istanbul (2007)Google Scholar
  19. Gad, A., Farooq, M.: Data fusion architecture for maritime surveillance. Proceedings of the 5th International Conference on Information Fusion, vol. 1 of Fusion, pp. 448–455. Annapolis (2002)Google Scholar
  20. Kalman, R. E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(D), pp. 35–45 (1960)Google Scholar
  21. Kämpchen, N.: Feature-level fusion of laser scanner and video data for advanced driver assistance systems. Ph.D thesis, Universität Ulm, Institut für Mess-, Regel- u. Mikrotechnik (2007)Google Scholar
  22. Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., Koch, O., Kuwata, Y., Moore, D., Olson, E., Peters, S., Teo, J., Truax, R., Walter, M., Barrett, D., Epstein, A., Maheloni, K., Moyer, K., Jones, T., Buckley, R., Antone, M., Galejs, R., Krishnamurthy, S., Williams, J.: A perception-driven autonomous Urban vehicle. J. Field Robotics 25(9), 727–774 (2008)CrossRefGoogle Scholar
  23. Llinas, J., Bowman, C., Rogova, G., Steinberg, A., Waltz, E., White, F.: Revisiting the JDL data fusion model II. Proceedings of the 7th International Conference on Information Fusion, pp. 1218–1230. Fusion, Mountain View (2004)Google Scholar
  24. Mahalanobis, P.C.: On the generalised distance in statistics. Proc. Natl. Inst. Sci. 2(1), 49–55 (1936)MathSciNetMATHGoogle Scholar
  25. Markin, M., Harris, C., Bernhardt, M., Austin, J., Bedworth, M., Greenway, P., Johntson, R., Little, A., Lowe, D.: Technology Foresight on Data Fusion and Data Processing. The Royal Aeronautical Society, London (1997)Google Scholar
  26. Mählisch, M.: Filtersynthese zur simultanen Minimierung von Existenz-, Assoziations- und Zustandsunsicherheiten in der Fahrzeugumfelderfassung mit heterogenen Sensordaten. Ph.D thesis, Universität Ulm, Institut für Mess-, Regel- u. Mikrotechnik (2009)Google Scholar
  27. Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. App. Math. 5(1), 32–38 (1957)MathSciNetMATHCrossRefGoogle Scholar
  28. Munz, M.: Generisches Sensorfusionsframework zur gleichzeitigen Zustands- und Existenzschätzung für die Fahrzeugumfelderkennung. PhD thesis, Universität Ulm, Institut für Mess-, Regel- u. Mikrotechnik (2011)Google Scholar
  29. Ohl, S., Matthaei, R., Müller, M., Maurer, M.: Softwarearchitektur der gitterbasierten Sensordatenfusion desProjekts Stadtpilot. In: Intelligente Transport- und Verkehrssysteme und -dienste Niedersachsen E.V. (eds.), AAET 2011, Automatisierungssysteme, pp. 281–297. Assistenzsysteme und eingebettete Systeme für Transportmittel, AAET, Braunschweig (2011)Google Scholar
  30. Ohl, S., Maurer, M.: A contour classifying kalman filter based on evidence theory. Proceedings of the 14th International IEEE Annual Conference on Intelligent Transportation Systems, pp. 1392–1397. ITSC, Washington, DC, USA (2011)Google Scholar
  31. Park, S.B.: ProFusion2 - D15.12 Final Report: Accessed online on 25.10.2011 http://prevent.ertico.webhouse.net/download/deliverables/ProFusion/202/PR-15000-SPD-v26/D15.12/ProFusion2/Final/Report.pdf (2007)
  32. Polychronopoulos, A., Amditis, A.: Revisiting JDL model for automotive safety applications: the PF2 functional model. Proceedings of the 9th International Conference on Information Fusion, pp. 1–7. Fusion, Florence (2006)Google Scholar
  33. PRO : PRORETA 1 - PRORETA: Accessed online on 26.04.2012 http://www.proreta.tu-darmstadt.de/proreta_1/projekt_proreta_1/index.de.jsp (2002–2006)
  34. Rosenblatt 1997 Rosenblatt, J.: DAMN: A distributed architecture for mobile navigation. Ph.D thesis, Carnegie Mellon University, Robotics institute, Pittsburgh (1997)Google Scholar
  35. Royce, W.W.: Managing the development of large software systems. Proceedings of the 9th International Conference on Software Engineering, pp. 328–338. ICSE, Monterey (1987)Google Scholar
  36. Saust, F., Bley, O., Kutzner, R., Wille, J.M., Friedrich, B., Maurer, M.: Exploitability of vehicle related sensor data in cooperative systems. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, pp. 1724–1729. ITSC, IEEE (2010)Google Scholar
  37. Saust, F., Wille, J.M., Lichte, B., Maurer, M.: Autonomous vehicle guidance on braunschweig’s inner ring road within the Stadtpilot project. Proceedings of the Intelligent Vehicles Symposium, IV, pp. 169–174. Baden-Baden (2011)Google Scholar
  38. Scheunert, U., Lindner, P., Cramer, H., Tatschke, T., Polychronopoulos, A., Wanielik, G.: Multi level processing methodology for automotive applications. Proceedings of the IEEE Intelligent Transportation Systems Conference, , pp. 1322–1327. ITSC, Toronto (2006)Google Scholar
  39. Schneider, U.: Sensordatenfusion und Fehlerkalibrierung von umfelderkennenden Sensoren eines Straßenfahrzeuges. Ph.D thesis, Technische Universität Braunschweig, Institut für Regelungstechnik (2006)Google Scholar
  40. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)MATHGoogle Scholar
  41. Steinberg, A.N., Bowman, C.L., White, F.E.: Revisions to the JDL data fusion model. Sensor Fusion: Archit. Algorithms Appl. III 3719(1), 430–441 (1999)CrossRefGoogle Scholar
  42. Tatschke, T., Park, S.-B., Amditis, A., Polychronopoulos, A., Scheunert, U., Aycard, O.: ProFusion2 - towards a modular, robust and reliable fusion architecture for automotive environment perception. In: Valldorf, J., Gessner, W. (eds.) Advanced Microsystems for Automotive Applications, pp. 451–469. Springer, Berlin Heidelberg (2006)Google Scholar
  43. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)MATHGoogle Scholar
  44. Ulbrich, S.: Intelligent decision making and maneuver planning for autonomous sriving in urban traffic environments. Diplomarbeit, Technische Universität Braunschweig, Institut für Regelungstechnik (2011)Google Scholar
  45. Weiss, T.T.: Hochgenaue Positionierung und Kartographie mit Laserscannern für Fahrerassistenzsysteme. Ph.D thesis, Universität Ulm (2011)Google Scholar
  46. White, F.E.: A model for data fusion. Proceedings of the 1st National Symposium on Sensor Fusion, vol. 2. Orlando (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Control EngineeringTechnische Universität BraunschweigBraunschweigGermany

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