Application of a Driver Intention Recognition Algorithm on a Pedestrian Intention Recognition and Collision Avoidance System

  • Frederik Diederichs
  • Nina Brouwer
  • Horst Klöden
  • Peter Zahn
  • Bernhard Schmitz
Part of the ATZ/MTZ-Fachbuch book series (ATZMTZ)


Driver intention recognition can enhance the driver-vehicle interaction by offering more intuitive assistance and automated driving support. Especially urban environments require fast reactions and hence assistance systems which act in accordance to driver’s intentions. Assistance should provide comfortably timed warnings only in situations when drivers really need this support and not in situations when the driver is already intending to react to a thread.

Fraunhofer IAO developed an algorithm in the UR:BAN MV subproject VIE to detect driver’s intention to brake when passing a pedestrian. The Fraunhofer algorithm analyses eye gaze data in correspondence with pedal activity to judge the driver’s attention on the pedestrian and the readiness to brake. BMW implemented the algorithm in the UR:BAN KA subproject SVT in a research vehicle and combined it with an environmental analysis of the situation.

In a test scenario the timing of a warning to the driver was adapted to the recognized intention to brake. Together with BMW’s pedestrian intention recognition algorithm, the driver intention recognition allows early warnings, while limiting the frequency of warnings to really relevant situations.


  1. 1.
    European commission: EU transport in figures. Statistical pocket book, 2014 (2015)., Accessed 29 Apr 2015Google Scholar
  2. 2.
    Auto Club Europa: Daten und Fakten: Fußgänger-Unfälle. Eine Studie des ACE Auto Club Europa (2012)., Accessed 29 Apr 2015Google Scholar
  3. 3.
    Nasar, J.L., Troyer, D.: Pedestrian injuries due to mobile phone use in public places. Accid Analysis Prev 57, 91–95 (2013)CrossRefGoogle Scholar
  4. 4.
    Euro NCAP Rating Review: Report from the Ratings Group. European New Car Assessment Programme Ratings Group Report (2015)., Accessed 08 Apr 2015Google Scholar
  5. 5.
    Winner, H., Hakuli, S., Lotz, F. (eds.): Handbuch Fahrerassistenzsysteme. Grundlagen, Komponenten und Systeme für aktive Sicherheit und Komfort, 3rd edn. Springer Fachmedien, Wiesbaden (2015). ATZ/MTZ-FachbuchGoogle Scholar
  6. 6.
    Manstetten, D., Bengler, K., Busch, F., Färber, B., Lehsing, C., Neukum, A.: “UR:BAN MV” – a German project focusing on human factors to increase traffic safety in urban areas. Proceedings of the 20th ITS World Congress, Tokyo, 2013. (2013)Google Scholar
  7. 7.
    Lehsing, C., Bngler, K., Busch, F., Schendzielorz, T.: UR:BAN – the German Research Initiative for User Centered Driver Assistance Systems and Traffic Network Management. Proceedings of the mobil.TUM Conference. (2013)Google Scholar
  8. 8.
    Schmidt, S., Färber, B.: Pedestrians at the kerb. Recognising the action intentions of humans. Transportation research part F: traffic psychology and behaviour 12.4 (2009):300–310 (2009)Google Scholar
  9. 9.
    Brouwer, N., Kloeden, H., Stiller, C.: Comparison and Evaluation of Pedestrian Motion Models for Vehicle Safety Systems. In Intelligent Transportation Systems (ITSC). IEEE 19th International Conference on (2207–2212), 2016, Sao Paolo, Brazil. IEEE (2016)Google Scholar
  10. 10.
    Kobiela, F.: Fahrerintentionserkennung für autonome Notbremssysteme. Technischen Universität Dresden, Dresden (2012)Google Scholar
  11. 11.
    Kopf, M.: Was nützt es dem Fahrer, wenn Fahrerinformations- und -assistenzsysteme etwas über ihn wissen? In: Maurer, M., Stiller, C. (eds.) Fahrerassistenzsysteme mit maschineller Wahrnehmung. basiert auf ausgewählten Vorträgen eines Workshops in Walting (Altmühltal), pp. 117–139. Springer, Berlin (2005)CrossRefGoogle Scholar
  12. 12.
    Heckhausen, H., Gollwitzer, P.M.: Thought contents and cognitive functioning in motivational versus volitional states of mind. Motiv Emot 11(2), 101–120 (1987)CrossRefGoogle Scholar
  13. 13.
    Achtziger, A., Gollwitzer, P.M.: Motivation und Volition im Handlungsverlauf. In: Heckhausen, J., Heckhausen, H. (eds.) Motivation und Handeln, pp. 309–335. Springer, Berlin, Heidelberg (2010). Springer-LehrbuchCrossRefGoogle Scholar
  14. 14.
    Diederichs, F., Pöhler, G.: Driving Maneuver Prediction Based on Driver Behavior Observation. Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics AHFE 2014, Krakau, 19.7.2014. (2014). J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73Google Scholar
  15. 15.
    Diederichs, F.: Entwicklung von verhaltensbasierten Verfahren zur Erkennung von Fahrerintention für die Prädiktion von Fahrmanövern. PhD-Thesis. University of Stuttgart (in press)Google Scholar
  16. 16.
    Diederichs, F., Schuttke, T., Spath, D.: Driver Intention Algorithm for Pedestrian Protection and Automated Emergency Braking Systems. In Intelligent Transportation Systems (ITSC). 2015 IEEE 18th International Conference. IEEE, pp 1049–1054 (2015)Google Scholar
  17. 17.
    Diederichs, F., Seitz, W., Spath, D.: Fahrerintentionserkennung auf Basis von Blickanalysen zur Vermeidung von Fußgängerkollisionen. 11. Berliner Werkstatt Mensch-Maschine-Systeme (BWMMS), Berlin-Brandenburgische Akademie der Wissenschaften, Oktober 2015 (2015)Google Scholar
  18. 18.
    Rehder, E., Kloeden, H., Stiller, C.: “Head detection and orientation estimation for pedestrian safety.” Intelligent Transportation Systems (ITSC). 2014 IEEE 17th International Conference on. vol. 2014. IEEE (2014)Google Scholar
  19. 19.
    Höfer, M.: Dissertation: Fahrerzustandsadaptive Assistenzfunktionen. IAT der Universität Stuttgart (2015)Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2018

Authors and Affiliations

  • Frederik Diederichs
    • 1
  • Nina Brouwer
    • 2
  • Horst Klöden
    • 2
  • Peter Zahn
    • 2
  • Bernhard Schmitz
    • 3
  1. 1.Human Factors EngineeringFraunhofer IAOStuttgartGermany
  2. 2.Autonomous Driving, Active Safety and SensorsBMW GroupMunichGermany
  3. 3.Institute for Visualization and Interactive Systems (VIS) / Co-FounderUniversity of StuttgartStuttgartGermany

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