Cognition, Technology & Work

, Volume 21, Issue 1, pp 21–31 | Cite as

When cooperation is needed: the effect of spatial and time distance and criticality on willingness to cooperate

  • Tanja StollEmail author
  • Fabian Müller
  • Martin Baumann
Original Article


In the future, car-to-car communication and car-to-infrastructure communication will be a central part of automated driving experience. Cooperative interactive driving is seen as a promising approach, in which cars interact cooperatively with drivers and the environment. However, to ensure drivers’ acceptance and their trust in such systems, it is important to understand the underlying mechanisms of human cooperation in traffic context. Therefore, this study investigated potential influencing parameters for cooperative behaviour in a lane change situation on a highway. As central influencing parameters the situation’s criticality and the distance in time and space to the driver asking for cooperation were manipulated. This was done by selecting appropriate levels for the time to collision (TTC) in conjunction with the variation of distances to other involved agents. In a video-based experiment with the perspective of driving on the left lane, 43 participants (M = 23.2 years; SD = 4.26 years) had to decide if they would give way to a driver in the right lane situated behind a slower truck. The results showed that the willingness to cooperate was strongly influenced by aspects of the situation: the driver’s costs (operationalized by the distance in time and space to the driver asking for cooperation) and the criticality of the situation for the other driver. A large distance in time and space to the driver asking for cooperation and, therefore, low costs of cooperation facilitate the driver’s willingness to cooperate via accelerating and decelerating. The results also indicated that in situations with high criticality drivers seemed to show strong uncertainty about how to behave or solve this situation. Consequently, cooperatively interacting systems with well-developed user interfaces might support drivers’ cooperative behaviour in critical situations.


Cooperative driving Criticality Lane change manoeuvre Time to collision 



This project was funded within the Priority Programme “CoInCar—Cooperatively Interacting Automobiles” of the German Science Foundation DFG.


  1. Baumann MRK, Krems JF (2009) A comprehension based cognitive model of situation awareness. In: Duffy VG (ed) Digital human modeling: second international conference, ICDHM 2009, held as part of HCI International 2009, San Diego, CA, USA, July 19–24, 2009, Proceedings, Springer, Berlin, pp 192–201Google Scholar
  2. Benenson Z, Girald A (2015) User acceptance factors for anonymous credentials: an empirical investigation. In: WEISGoogle Scholar
  3. Bengler K, Zimmermann M, Bortot D, Kienle M, Damböck D (2012) Interaction principles for cooperative human–machine systems. In: Information technologyGoogle Scholar
  4. Croissant Y (2012) Estimation of multinomial logit models in R: the m logit Packages. R package version 0.2-2.
  5. Durso FT, Rawson KA, Girotto S (2007) Comprehension and situation awareness. In: Durso FT, Nickerson RS, Dumais ST, Lewandowsky S, Perfect TJ (eds) Handbook of applied cognition, vol 2. Wiley, Amsterdam, pp 163–193CrossRefGoogle Scholar
  6. Fiosins M, Friedrich B, Görmer J, Mattfeld D, Müller JP, Tchouankem H (2016) A multiagent approach to modeling autonomic road transport support systems. In: McCluskey TL, Kotsialos A, Müller JP, Klügl F, Rana O, Schumann R (eds) Autonomic road transport support systems, Springer, New York, pp 67–85Google Scholar
  7. Heesen M, Baumann M, Kelsch J, Nause D, Friedrich M (2012) Investigation of cooperative driving behaviour during lane change in a multi-driver simulation environment. In: Human Factors and Ergonomics Society (HFES) Europe Chapter Conference Touluse, pp 305–318Google Scholar
  8. Hoc J-M (2001) Towards a cognitive approach to human–machine cooperation in dynamic situations. Int J Hum Comput Stud 54:509–540CrossRefGoogle Scholar
  9. Horst R van der (1990) A time-based analysis of road user behaviour at intersections. In: ICT CT conference proceedings, pp 91–104Google Scholar
  10. van der Horst R (1991) Time-to-collision as a cue for decision-making in braking. In: Gale AG et al (eds) Vision in vehicles–III. Elsevier, AmsterdamGoogle Scholar
  11. Hurts K, Boer R de (2014) “What is it doing now?” Results of a survey into automation surprise. In Proceedings of the 31st EAAP conference, Valletta, Malta, 22–26 Sept 2014Google Scholar
  12. Kiefer RJ, Flannagan CA, Jerome CJ (2006) Time-to-collision judgments under realistic driving conditions. Hum Fact 48:334–345. CrossRefGoogle Scholar
  13. Lee S, Olsen ECB, Wierwille WW (2004) A comprehensive examination of naturalistic lane changes. National Highway Traffic Safety, Washington. Accessed 23 Dec 2017
  14. Li L, Wang F-Y (2006) Cooperative driving at blind crossings using intervehicle communication. IEEE Trans Veh Technol 55:1712–1724. CrossRefGoogle Scholar
  15. Lütteken N, Zimmermann M, Bengler KJ (2016) Using gamification to motivate human cooperation in a lane-change scenario. In: 19th International conference on intelligent transportation systems (ITSC), pp 899–906Google Scholar
  16. Marangunić N, Granić A (2015) Technology acceptance model: a literature review from 1986 to 2013. Univ Access Inf Soc 14:81–95CrossRefGoogle Scholar
  17. Martin J (2001) Organizational culture: mapping the terrain. Sage Publications, Thousand OaksGoogle Scholar
  18. Moriarty DE, Handley S, Langley P (1998) Learning distributed strategies for traffic control. In: Proceedings of the 5th international conference of the society for adaptive behavior, pp 437–446Google Scholar
  19. Skrondal A, Rabe-Hesketh S (2003) Multilevel logistic regression for polytomous data and rankings. Psychometrika 68:267–287. MathSciNetCrossRefzbMATHGoogle Scholar
  20. Tamke A, Dang T, Breuel G (2011) A flexible method for criticality assessment in driver assistance systems. In: Intelligent vehicles symposium (IV). IEEE, pp 697–702Google Scholar
  21. Vogel K (2003) A comparison of headway and time to collision as safety indicators. Accid Anal Prev 35:427–433CrossRefGoogle Scholar
  22. Winsum W van, Heino A (1996) Choice of time-headway in car-following and the role of time-to-collision information in braking. Ergonomics 39:579–592. CrossRefGoogle Scholar
  23. Yan F, Weber L, Luedtke A (2015) Classifying driver’s uncertainty about the distance gap at lane changing for developing trustworthy assistance systems. In: Intelligent vehicles symposium (IV).
  24. Zimmermann M, Fahrmeier L, Bengler K (2015) A Roland for an Oliver? Subjective percept ion of cooperation during conditionally automated driving. In: Smari WW (ed) 2015 International conference on collaboration technologies and systems (CTS), 1–5 June 2015, Atlanta, Georgia. IEEE, Piscataway, NJGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Faculty of Engineering, Computer Science and Psychology, Department of Human FactorsUlm UniversityUlmGermany

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