Action prediction with the Jordan model of human intention: a contribution to cooperative control

  • Friederike Schneemann
  • Frederik DiederichsEmail author
Original Article


Human intentions are internal processes that can be deduced by observation of their resulting actions. Hence, an observation-based model of human intention is needed. The Jordan model of human intention in traffic is presented and applied on empirical data for two different intentions: driver’s braking intention and pedestrian’s crossing intention. The analysis shows that the behavior postulated within the theoretically developed Jordan model is observable in both sets of empirical data of drivers and of pedestrians while they perform intended actions. It can be assumed, that the Jordan model is universally applicable on very distinct intentions, not only limited to traffic scenarios. The Jordan model enhances the development of cooperative control among humans and machines. On the way towards a design of cooperative behavior among humans and vehicles in traffic, the Jordan model contributes with a systematic sequence and description of human behavior that is reflecting intention. Consequently an integration of the Jordan model of human intention with the model of cooperative and shared control Flemisch et al. (Ergonomics 57(3):343–360, 2014) is proposed. This integration facilitates the cooperation between human and system on strategic, tactical, and operational level.


Intention Behavior prediction Cooperative control Driver assistance systems Automated driving 


  1. Ajzen I (1985) From intentions to actions: a theory of planned behavior. In: Kuhl J, Beckmann J (eds) Action control. Springer, Heidelberg, pp 11–39CrossRefGoogle Scholar
  2. Anscombe GEM (1957) Intention. Cornell University Press, New YorkGoogle Scholar
  3. Blaschke C, Schmitt J, Färber B (2007) Fahrmanöver-Prädiktion über CAN-BUS Daten. VDI-Berichte, vol 2015. VDI-Verlag, Düsseldorf, pp 165–172Google Scholar
  4. Brouwer N, Kloeden H, Stiller C (2016) Comparison and evaluation of pedestrian motion models for vehicle safety systems. In: Paper presented at IEEE 19th international conference on intelligent transportation systems (ITSC), Rio de Janeiro, Brazil, pp 2207–2212Google Scholar
  5. Das S, Manski CF, Manuszak MD (2005) Walk or wait? An empirical analysis of street crossing decisions. J Appl Econometr 20(4):529–548MathSciNetCrossRefGoogle Scholar
  6. Davidson D (1963) Actions, reasons and causes. J Philos 60(23):685–700CrossRefGoogle Scholar
  7. Diederichs F (2017) Entwicklung von verhaltensbasierten Verfahren zur Erkennung von Fahrerintention für die Prädiktion von Fahrmanövern. Dissertation, University of Stuttgart. Schriftenreihe zu Arbeitswissenschaft und Technologiemanagement 36, Fraunhofer Verlag, Stuttgart, GermanyGoogle Scholar
  8. Diederichs F, Pöhler G (2014) Driving maneuver prediction based on driver behavior observation. In: Proceedings of the 5th international conference on applied human factors and ergonomics (AHFE), pp 68–73Google Scholar
  9. Diederichs F, Schüttke T, Spath D (2015) Driver intention algorithm for pedestrian protection and automated emergency braking systems. In: Paper presented at IEEE 18th 19th international conference on intelligent transportation systems (ITSC), Las Palmas, Spain, pp 1049–1054Google Scholar
  10. Donges E (1982) Aspekte der Aktiven Sicherheit bei der Führung von Personenkraftwagen. Automobil-Industrie 27:183–190Google Scholar
  11. Egan CD, Willis A, Ness H, Stradling S (2008) Visual gaze behaviour of children and adult pedestrians at signalized and unsignalized road crossings. Technical report, Napier University, EdinburghGoogle Scholar
  12. Fishbein M, Ajzen I (1975) Belief, attitude, intention, and behavior. Addison-Wesley, BostonGoogle Scholar
  13. Flemisch FO, Bengler K, Bubb H, Winner H, Bruder R (2014) Towards cooperative guidance and control of highly automated vehicles. H-mode and conduct-by-wire. Ergonomics 57(3):343–360CrossRefGoogle Scholar
  14. Flemisch F, Abbink D, Itoh M, Pacaux-Lemoine M-P, Weßel G (2018) Joining the blunt and the pointy end of the spear: towards a common framework of joint action, human-machine cooperation, cooperative guidance and control, shared-, traded- and supervisory control. In: Cognition technology and work. special issue on shared and cooperative control of humans and machinesGoogle Scholar
  15. Gerdes A (2006) Driving manoeuvre recognition. Technical report, German Aerospace Center, BraunschweigGoogle Scholar
  16. Grayson GB (1975) Observations of pedestrian behaviour at four sites. Technical report, Road User Characteristics Division, Transport and Road Research Laboratory, CrowthorneGoogle Scholar
  17. Hagen K, Schulze C, Schlag B (2010) Verkehrssicherheit von schwächeren Verkehrsteilnehmern im Zusammenhang mit dem geringen Geräuschniveau von Fahrzeugen mit alternativen Antrieben (FAT-Schriftenreihe Vol. 245)Google Scholar
  18. Hamaoka H, Hagiwara T, Tada M (2013) A study on the behavior of pedestrians when confirming approach of right/left-turning vehicle while crossing a crosswalk. In: Proceedings of the Eastern Asia Society for transportation studies, vol 9Google Scholar
  19. Heckhausen H, Gollwitzer PM (1987) Thought contents and cognitive functioning in motivational versus volitional states of mind. Mot Emot 11(2):101–120CrossRefGoogle Scholar
  20. Itoh M, Flemisch F, Abbink D (2013) A hierarchical framework to analyze shared control conflicts between human and machine. IFAC PapersOnLine 49(19):96–101CrossRefGoogle Scholar
  21. Kadali BR, Vedagiri P (2013) Modelling pedestrian road crossing behaviour under mixed traffic condition. Eur Transp 55:1–17Google Scholar
  22. Kloeden H, Brouwer N, Ries S, Rasshofer RH (2014) Potenzial der Kopfposenerkennung zur Absichtsvorhersage von Fußgängern im urbanen Verkehr. Workshop Fahrerassistenzsysteme, Walting, pp 67–78Google Scholar
  23. Kobiela F (2011) Fahrerintentionserkennung für autonome Notbremssysteme Dissertation, Technische Universität Dresden. VS Verlag für Sozialwissenschaften, Wiesbaden, GermanyCrossRefGoogle Scholar
  24. Koehler S, Goldhammer M, Bauer S, Doll K, Brunsmann U, Dietmayer K (2012) Early detection of the pedestrian’s intention to cross the street. In: Paper presented at 15th international IEEE conference on intelligent transportation systems (ITSC), Anchorage, AK, USA, pp 1759–1764Google Scholar
  25. Kopf M (2005) Was nützt es dem Fahrer, wenn Fahrerinformations- und -assistenzsysteme etwas über ihn wissen? In: Fahrerassistenzsysteme mit maschineller Wahrnehmung (S. 117–139). SpringerGoogle Scholar
  26. Lee SE, Olsen EC, Wierwille WW (2004) A comprehensive examination of naturalistic lane-changes. Technical report, NHTSA, WashingtonGoogle Scholar
  27. Makoto I, Flemisch F, Abbink D (2016) A hierarchical framework to analyze shared control conflicts between human and machine. IFAC PapersOnLine, 2016 49(19):96–101Google Scholar
  28. Martirosjan A, Griesche S (2012) Driver intention modelling for partly automated vehicles—the benefit and necessity of a driver and situation adaptive approach. In: Paper presented at 30th European annual conference on human decision-making and manual control (EAM 2012), Braunschweig, GermanyGoogle Scholar
  29. Montel MC, Brenac T, Granie M-A, Millot M, Coquelet C (2013) Urban Environments, Pedestrian friendliness and Crossing Decisions. In: Report presented at 92nd annual meeting of the transportation research board, FranceGoogle Scholar
  30. Nee J, Hallenbeck ME (2003) A motorist and pedestrian behavioral analysis relating to pedestrian safety improvements. Technical report no. WA-RD 560.1, Washington State Transportation Center (TRAC), SeattleGoogle Scholar
  31. Oliver N, Pentland AP (2000) Driver behavior recognition and prediction in a smart-car. In: Verly JG (ed) Proceedings of SPIE—the International Society for Optical Engineering, vol 4023, pp 280–290)Google Scholar
  32. Pacaux-Lemoine M-P, Flemisch F (2016) Layers of shared and cooperative control, assistance and automation. IFAC PapersOnLine 49(19):159–164CrossRefGoogle Scholar
  33. Pentland A, Liu A (1999) Modeling and prediction of human behavior. Neural Comput 11(1):229–242CrossRefGoogle Scholar
  34. Puca RM (2014) Intention. In: Wirtz MA (ed) Dorsch-Lexikon der Psychologie, 17th edn. Verlag Hans Huber, Göttingen, p 801Google Scholar
  35. Rasmussen J (1979) On the structure of knowledge: a morphology of mental models. (Riso-M-2192). Riso National Laboratory, Electronics Department, RoskildeGoogle Scholar
  36. Rehder E, Kloeden H (2015) Goal-directed pedestrian prediction. In: Paper presented at 2015 IEEE international conference on computer vision workshop (ICCVW), Santiago, Chile, pp 139–147Google Scholar
  37. Rothenbücher D, Li J, Sirkin D, Mok B, Ju W (2016) Ghost driver: a field study investigating the interaction between pedestrians and driverless vehicles. In: Paper presented at 25th IEEE international symposium on robot and human interactive communication (RO-MAN), New York, USA, pp 795–802Google Scholar
  38. Salvucci DD (2004) Inferring driver intent: a case study in lane-change detection. In: Proceedings of the Human Factors and Ergonomics Society annual meeting, vol 48(1), pp 2228–2231CrossRefGoogle Scholar
  39. Scherf O, Zecha S (2009) Method for determining a probable movement area/location area of a living being and vehicle for carrying out said method. Patent no. WO2,009,019,214 A3Google Scholar
  40. Schmidt S, Färber B (2009) Pedestrians at the kerb—recognising the action intentions of humans. Transp Res Part F Traffic Psychol Behav 12(4):300–310CrossRefGoogle Scholar
  41. Schneemann F, Gohl I (2016a) Analyzing driver-pedestrian interaction at crosswalks: a contribution to autonomous driving in urban environments. In: Paper presented at IEEE intelligent vehicles symposium (IV), Gothenburg, Sweden, pp 38–43Google Scholar
  42. Schneemann F, Heinemann P (2016b) Context-based detection of pedestrian crossing intention for autonomous driving in urban environments. In: Paper presented at IEEE/RSJ international conference on intelligent robots and systems (IROS), Daejeon, Korea, pp 1–6Google Scholar
  43. Schoon JG (2006) Pedestrian behaviour at uncontrolled crossings. Traffic Eng Control 47(6):229–235Google Scholar
  44. Schroven F, Giebel T (2008) Fahrerintentionserkennung für Fahrerassistenzsysteme/Driver intent Recognition for advanced driver assistance systems. VDI-Berichte, vol 2048. VDI-Verlag, Düsseldorf pp 153–161Google Scholar
  45. Schubert R, Richter E, Wanielik G (2008) Comparison and evaluation of advanced motion models for vehicle tracking. In: Paper presented at 11th international conference on information fusion, Cologne, Germany, pp 1–6Google Scholar
  46. Schweizer T, Thomas C, Regli P (2009) Verhalten am Fussgängerstreifen. Technical report, Fussverkehr Schweiz, :Google Scholar
  47. Sisiopiku VP, Akin D (2003) Pedestrian behaviors at and perceptions towards various pedestrian facilities: an examination based on observation and survey data. Transp Res Part F Traffic Psychol Behav 6(4):249–274CrossRefGoogle Scholar
  48. Sullman MJM, Gras ME, Font-Mayolas S, Masferrer L, Cunill M, Planes M (2011) The pedestrian behaviour of Spanish adolescents. J Adolesc 34(3):531–539CrossRefGoogle Scholar
  49. Wilson DG, Grayson GB (1980) Age-related differences in the road crossing behaviour of adult pedestrians. Technical report, Transport and Road Research Laboratory, CrowthorneGoogle Scholar
  50. Witzlack C, Beggiato M, Krems J (2016) Interaktionssequenzen zwischen Fahrzeugen und Fußgängern im Parkplatzszenario als Grundlage für kooperativ interagierende Automatisierung. VDI-Berichte, vol 2288. VDI-Verlag, Düsseldorf, pp 323–336Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Audi AG Architektur, Vor-/Konzeptentwicklung automatisiertes FahrenIngolstadtGermany
  2. 2.Human Factors Engineering and Vehicle InteractionStuttgartGermany

Personalised recommendations