Functional System Architecture for an Autonomous on-Road Motor Vehicle

Chapter

Abstract

Autonomous driving is a widely discussed field of research with still growing interest. In addition to a lot of technical, legal and social questions to be solved, an immense challenge still remains in mastering the complexity of the resulting system which would eventually replace the driver. A supporting tool for developing complex systems is given by the functional system architecture, which describes the system on an abstract level independent of concrete technical solutions. Functional system architectures published in the context of autonomous driving do not cover all necessary functional requirements. However, they focus on different sub-aspects and functional mechanisms within this context.

Our functional system architecture, which has been developed in the research project Stadtpilot at the Technische Universität Braunschweig, focuses on systematization and a combination of localization- and perception-driven approaches into one single well-structured functional system architecture. It has been developed in a top-down approach based on a formulation of the functional requirements of an autonomous on-road motor vehicle, in the sense of a modular building block system. It covers the aspects of localization, environmental and self-perception, mission accomplishment, usage of map data and communication, and the integration of the human being as a passenger and as another traffic participant in the close surroundings of the autonomous vehicle.

Referring to our functional system architecture, we discuss some basic mechanisms of autonomous driving in the following article, which become transparent due to the architecture’s basic structure. Additionally, we discuss where current advanced driver assistance systems are located within this architecture. This makes the big efforts which still have to be made to fulfill the necessary functional requirements regarding an autonomous vehicle driving safely in public road traffic more transparent.

Keywords

System architecture Autonomous driving Localization Map data Perception Cooperation 

References

  1. Bacha, A., Bauman, C., Faruque, R., Fleming, M., Terwelp, C., Reinholtz, C., Hong, D., Wicks, A., Alberi, T., Anderson, D., Cacciola, S., Currier, P., Dalton, A., Farmer, J., Hurdus, J., Kimmel, S., King, P., Taylor, A., Van Covern, D., Webster, M.: Odin: Team VictorTango’s entry in the DARPA urban challenge. J. Field Rob. 25(8), 467–492 (2008)CrossRefGoogle Scholar
  2. Bonasso, P., Firby, J., Gat, E., Kortenkamp, D., Miller, D.P., Slack, M.G.: Experiences with an architecture for intelligent, reactive agents. J. Exp. Theor. Artif. Intell. 9(2–3), 237–256 (1997). doi: 10.1080/095281397147103 CrossRefGoogle Scholar
  3. Broggi, A., Buzzoni, M., Debattisti, S., Grisleri, P., Laghi, M.C., Medici, P., Versari, P.: Extensive tests of autonomous driving technologies. IEEE Trans. Intell. Transp. Syst. 14(3), 1403–1415 (2013). doi: 10.1109/TITS.2013.2262331 CrossRefGoogle Scholar
  4. Dickmanns, E.D.: Dynamic Vision for Perception and Control of Motion. Springer, London (2007)Google Scholar
  5. Donges, E.: A conceptual framework for active safety in road traffic. Veh. Syst. Dyn. 32(2–3), 113–128 (1999). doi: 10.1076/vesd.32.2.113.2089 CrossRefGoogle Scholar
  6. Du, J., Masters, J., Barth, M.: Lane-level positioning for in-vehicle navigation and automated vehicle location (AVL) systems. In: 7th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, pp. 35–40 (2004)Google Scholar
  7. Gasser, T.M., Arzt, C., Ayoubi, M., Bartels, A., Bürkle, L., Eier, J., Flemisch, F., Häcker, D., Hesse, T., Huber, W., Lotz, C., Maurer, M., Ruth-Schumacher, S., Schwarz, J., Vogt, W.: Rechtsfolgen zunehmender Fahrzeugautomatisierung. Berichte der Bundesanstalt für Straßenwesen F83. Wirtschaftsverlag NW, Bergisch Gladbach (2012)Google Scholar
  8. Hale, A.R., Stoop, J., Hommels, J.: Human error models as predictors of accident scenarios for designers in road transport systems. Ergonomics. 33(10–11), 1377–1387 (1990). doi: 10.1080/00140139008925339 CrossRefGoogle Scholar
  9. Herrmann, S.: Kollisionswarnung im urbanen Straßenverkehr auf Basis einer probabilistischen Situationsanalyse. PhD Dissertation, Technische Universität Braunschweig (2013)Google Scholar
  10. Herrmann, S., Schroven, F.: Situation analysis for driver assistance systems at urban intersections. In: IEEE International Conference on Vehicular Electronics and Safety (ICVES), Istanbul, pp. 151–156 (2012). doi: https://doi.org/10.1109/ICVES.2012.6294295
  11. Hock, C.J.L.: Wissensbasierte Fahrzeugführung mit Landmarken für autonome Roboter. PhD Dissertation, Universität der Bundeswehr (1994)Google Scholar
  12. ISO 17387: Intelligent Transport Systems – Lane Change Decision Aid Systems (LCDAS) – Performance Requirements and Test Procedures. Standard ISO 17387:2008. International Organization for Standardization, Geneva (2008)Google Scholar
  13. ISO 15622: Intelligent Transport Systems – Adaptive Cruise Control Systems – Performance Requirements and Test Procedures. Standard ISO 15622:2010. International Organization for Standardization, Geneva (2010)Google Scholar
  14. ISO 26262: Road Vehicles – Functional Safety – Part 4: Product Development at the System Level. Standard ISO 26262–4:2011(E). International Organization for Standardization, Geneva (2011)Google Scholar
  15. Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S.: A perception-driven autonomous urban vehicle. J. Field Rob. 25(10), 727–774 (2008)CrossRefGoogle Scholar
  16. Levinson, J.S.: Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles. Stanford University, Stanford, CA (2011)Google Scholar
  17. Mages, M., Stoff, A., Klanner, F.: Intersection assistance. In: Winner, H., Hakuli, S., Lotz, F., Singer, C. (eds.) Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort. Springer International Publishing, Cham, Switzerland (2015)Google Scholar
  18. Matthaei, R.: Wahrnehmungsgestützte Lokalisierung in fahrstreifengenauen Karten für Asssistenzsysteme und automatisches Fahren in urbaner Umgebung. PhD Dissertation, Technische Universität Braunschweig (2015)Google Scholar
  19. Matthaei, R., Maurer, M.: Autonomous driving – a top-down-approach. Automatisierungstechnik. 63(3), 155–167 (2015). doi: 10.1515/auto-2014-1136 CrossRefGoogle Scholar
  20. Matthaei, R., Reschka, A., Rieken, J., Dierkes, F., Ulbrich, S., Winkle, T., Maurer, M.: Autonomous driving. In: Winner, H., Hakuli, S., Lotz, F., Singer, C. (eds.) Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort. Springer International Publishing, Cham, Switzerland (2015)Google Scholar
  21. Maurer, M.: Flexible Automatisierung von Straßenfahrzeugen mit Rechnersehen. PhD Dissertation, Universität der Bundeswehr München (2000)Google Scholar
  22. Milford, M.J., Wyeth, G.F.: Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Trans. Rob. 24(5), 1038–1053 (2008). doi: 10.1109/TRO.2008.2004520 CrossRefGoogle Scholar
  23. Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., et al.: Junior: the Stanford entry in the urban challenge. J. Field Rob. 25(9), 569–597 (2008)CrossRefGoogle Scholar
  24. Moore, D.C., Huang, A.S., Walter, M., Olson, E., Fletcher, L., Leonard, J., Teller, S.: Simultaneous local and global state estimation for robotic navigation. In: IEEE International Conference on Robotics and Automation (ICRA), Kobe, pp. 3794–3799 (2009)Google Scholar
  25. Muigg, A.: Implizites Workloadmanagement. PhD Dissertation, Technische Universität München (2009)Google Scholar
  26. Müller, A., Himmelsbach, M., Lüttel, T., von Hundelshausen, F., Wünsche, H.-J.: GIS-based topological robot localization through LIDAR crossroad detection. In: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, pp. 2001–2008 (2011)Google Scholar
  27. Nothdurft, T., Hecker, P., Frankiewicz, T., Gacnik, J., Koster, F.: Reliable information aggregation and exchange for autonomous vehicles. In: Vehicular Technology Conference (VTC Fall), 2011 IEEE, San Francisco, pp. 1–5 (2011)Google Scholar
  28. Rasmussen, J.: Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Trans. Syst. Man Cybern. 13(3), 257–266 (1983)CrossRefGoogle Scholar
  29. Rauskolb, F.W., Berger, K., Lipski, C., Magnor, M., Cornelsen, K., Effertz, J., Form, T., et al.: Caroline: an autonomously driving vehicle for urban environments. J. Field Rob. 25(9), 674–724 (2008). doi: 10.1002/rob.20254 CrossRefGoogle Scholar
  30. SAE International: Taxonomy and Definitions for Terms Related to on-Road Motor Vehicle Automated Driving Systems (2016)Google Scholar
  31. Spieß, E.: Kooperation. Herausgegeben von Antonius Wirtz. Dorsch – Lexikon der Psychologie. Verlag Hans Huber. https://portal.hogrefe.com/dorsch/kooperation/ (2014)
  32. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents Series). The MIT Press, Cambridge, MA (2005)MATHGoogle Scholar
  33. van Zanten, A., Kost, F.: Brake-based assistance functions. In: Winner, H., Hakuli, S., Lotz, F., Singer, C. (eds.) Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort. Springer International Publishing, Cham, Switzerland (2015)Google Scholar
  34. Visintainer, F., Darin, M.: Final requirements and strategies for map feedback. Report D2.2. Ertico (2008)Google Scholar
  35. Von Hundelshausen, F., Himmelsbach, M., Hecker, F., Mueller, A., Wuensche, H.-J.: Driving with tentacles: integral structures for sensing and motion. J. Field Rob. 25(9), 640–673 (2008). doi: 10.1002/rob.20256 CrossRefGoogle Scholar
  36. Wachenfeld, W., Winner, H., Gerdes, C., Lenz, B., Maurer, M., Beiker, S.A., Fraedrich, E., Winkle, T.: Use cases des Autonomen Fahrens. In: Maurer, M., Gerdes, J.C., Lenz, B., Winner, H. (eds.) Autonomes Fahren – Technische, rechtliche und gesellschaftliche Aspekte. Springer-Verlag GmbH, Berlin (2015)Google Scholar
  37. Werling, M., Ziegler, J., Kammel, S., Thrun, S.: Optimal trajectory generation for dynamic street scenarios in a Frenét frame. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 987–993 (2010). doi: 10.1109/ROBOT.2010.5509799
  38. Wille, J.M., Saust, F., Maurer, M.: Stadtpilot: driving autonomously on braunschweig’s inner ring road. In: IEEE Intelligent Vehicles Symposium (IV), San Diego, CA, pp. 506–511 (2010)Google Scholar
  39. Winner, H., Schopper, M.: Adaptive cruise control. In: Winner, H., Hakuli, S., Lotz, F., Singer, C. (eds.) Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort. Springer International Publishing, Cham, Switzerland (2015)Google Scholar
  40. Ziegler, J., Bender, P., Lategahn, H., Schreiber, M., Strauß, T., Stiller, C.: Kartengestütztes automatisiertes Fahren Fahren auf der Bertha-Benz-Route von Mannheim nach Pforzheim. In: 9. Workshop Fahrerassistenzsysteme, Walting (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institut für RegelungstechnikTechnische Universität BraunschweigBraunschweigGermany

Personalised recommendations