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

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.

[1]  Andreas Lüdtke,et al.  Classifying driver's uncertainty about the distance gap at lane changing for developing trustworthy assistance systems , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[2]  Jean-Arcady Meyer,et al.  Learning Distributed Strategies for Traffic Control , 1998 .

[3]  Thao Dang,et al.  A flexible method for criticality assessment in driver assistance systems , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[4]  Lutteken Niklas,et al.  Using gamification to motivate human cooperation in a lane-change scenario , 2016 .

[5]  Suzanne E. Lee,et al.  A COMPREHENSIVE EXAMINATION OF NATURALISTIC LANE-CHANGES , 2004 .

[6]  Francis T. Durso,et al.  Comprehension and Situation Awareness , 2008 .

[7]  Linda L. Putnam,et al.  Organizational Culture: Mapping the Terrain , 2001 .

[8]  Andrina Granic,et al.  Technology acceptance model: a literature review from 1986 to 2013 , 2014, Universal Access in the Information Society.

[9]  Y. Croissant.  Estimation of multinomial logit models in R : The mlogit Package , 2010 .

[10]  Jean-Michel Hoc,et al.  Towards a cognitive approach to human-machine cooperation in dynamic situations , 2001, Int. J. Hum. Comput. Stud..

[11]  Katja Vogel,et al.  A comparison of headway and time to collision as safety indicators. , 2003, Accident; analysis and prevention.

[12]  Fei-Yue Wang,et al.  Cooperative Driving at Blind Crossings Using Intervehicle Communication , 2006, IEEE Transactions on Vehicular Technology.

[13]  Bernhard Friedrich,et al.  A Multiagent Approach to Modeling Autonomic Road Transport Support Systems , 2016, Autonomic Road Transport Support Systems.

[14]  Josef F. Krems,et al.  A Comprehension Based Cognitive Model of Situation Awareness , 2009, HCI.

[15]  Martin Baumann,et al.  Investigation of Cooperative Driving Behaviour during Lane Change in a Multi-Driver Simulation Environment , 2012 .

[16]  Rvd Horst,et al.  TIME-TO-COLLISION AS A CUE FOR DECISION-MAKING IN BRAKING , 1991 .

[17]  Vincent G. Duffy,et al.  Digital human modeling : Second International Conference, ICDHM 2009, held as part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009 : proceedings , 2009, INTERACT 2009.

[18]  Christian J. Jerome,et al.  Time-to-Collision Judgments Under Realistic Driving Conditions , 2006, Hum. Factors.

[19]  Markus Zimmermann,et al.  A Roland for an Oliver? Subjective perception of cooperation during conditionally automated driving , 2015, 2015 International Conference on Collaboration Technologies and Systems (CTS).

[20]  Ioannis Krontiris,et al.  User Acceptance Factors for Anonymous Credentials: An Empirical Investigation , 2015, WEIS.

[21]  Markus Zimmermann,et al.  Using gamification to motivate human cooperation in a lane-change scenario , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[22]  Sophia Rabe-Hesketh,et al.  Multilevel logistic regression for polytomous data and rankings , 2003 .

[23]  Robert J. de Boer,et al.  “ What is it doing now ? ” Results of a Survey into Automation Surprise 1 , 2014 .

[24]  K. Bengler,et al.  Interaction Principles for Cooperative Human-Machine Systems , 2012, it Inf. Technol..

[25]  A Heino,et al.  Choice of time-headway in car-following and the role of time-to-collision information in braking. , 1996, Ergonomics.