Philipp Wintersberger
Andreas Riener
ABSTRACT
It is expected that automated vehicles (AVs) will only be used when customers believe them to be safe, trustworthy, and match their personal driving style. As AVs are not very common today, most previous studies on trust, user experience, or acceptance measures in automated driving are based on qualitative measures. The approach followed in this work is different, as we compared the direct effect of human drivers versus automated driving systems (ADSs) on the front seat passenger. In a driving simulator study (N=48), subjects had either to ride with an ADS, a male, or a female driver. Driving scenarios were the same for all subjects. Findings from quantitative measurements (HRV, face tracking) and qualitative pre-/post study surveys and interviews suggest that there are no significant differences between the passenger groups. Our conclusion is, that passengers are already inclined to accept ADS and that the market is ready for AVs.
- Prateek Bansal, Kara M Kockelman, and Amit Singh. 2016. Assessing public opinions of and interest in new vehicle technologies: An Austin perspective. Transportation Research Part C: Emerging Technologies 67 (2016), 1--14. Google Scholar
Cross Ref
- Jenay M Beer, Akanksha Prakash, Tracy L Mitzner, and Wendy A Rogers. 2011. Understanding robot acceptance. Technical Report.Google Scholar
- Stephen M Casner, Edwin L Hutchins, and Don Norman. 2016. The challenges of partially automated driving. Commun. ACM 59, 5 (2016), 70--77.Google ScholarDigital Library
- R Castaldo, Paolo Melillo, Umberto Bracale, M Caserta, Maria Triassi, and Leandro Pecchia. 2015. Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis. Biomedical Signal Processing and Control 18 (2015), 370--377. Google Scholar
- Fredrick Ekman, Mikael Johansson, and Jana L Sochor. 2016. Creating Appropriate Trust for Autonomous Vehicle Systems: A Framework for HMI Design. In Proceedings of the 95th Annual Meeting of the Transportation Research Board, Washington, DC January 10-14, 2016.Google Scholar
- David Garcia, Christine Kreutzer, Karla Badillo-Urquiola, and Mustapha Mouloua. 2015. Measuring Trust of Autonomous Vehicles: A Development and Validation Study. In HCI International 2015-Posters Extended Abstracts. Springer, 610--615. Google ScholarCross Ref
- Anja Grießmeier. 2016. Selbstfahrende Autos in den Augen der Österreicher. IMAS Report International 7/2016 (2016), pp. 14.Google Scholar
- Tove Helldin, Göran Falkman, Maria Riveiro, and Staffan Davidsson. 2013. Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 210--217. Google ScholarDigital Library
- Sebastian Hergeth, Lutz Lorenz, Roman Vilimek, and Josef F Krems. 2016. Keep Your Scanners Peeled Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Human Factors: The Journal of the Human Factors and Ergonomics Society 58, 3 (2016), 509--519. Google ScholarCross Ref
- Andrew L Kun, Susanne Boll, and Albrecht Schmidt. 2016. Shifting Gears: User Interfaces in the Age of Autonomous Driving. IEEE Pervasive Computing 15, 1 (2016), 32--38.Google ScholarDigital Library
- Miltos Kyriakidis, Riender Happee, and JCF De Winter. 2015. Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transportation research part F: traffic psychology and behaviour 32 (2015), 127--140. Google ScholarCross Ref
- Todd Litman. 2014. Autonomous Vehicle Implementation Predictions. Victoria Transport Policy Institute 28 (2014).Google Scholar
- Regan L Mandryk and M Stella Atkins. 2007. A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International journal of human-computer studies 65, 4 (2007), 329--347.Google Scholar
- Masahiro Mori. 1970. The Uncanny Valley. Energy 7, 4 (1970), 33--35. in Japanese.Google Scholar
- M Loretto Munoz, Arie van Roon, Harriëtte Riese, Chris Thio, Emma Oostenbroek, Iris Westrik, Eco JC de Geus, Ron Gansevoort, Joop Lefrandt, Ilja M Nolte, and others. 2015. Validity of (Ultra-) Short Recordings for Heart Rate Variability Measurements. PloS one 10, 9 (2015), e0138921.Google ScholarCross Ref
- Jeon Myounghoon, Andreas Riener, Jason Sterkenburg, Ju-Hwan Lee, Bruce Walker, and Ignacio Alvarez. 2017. An International Survey on Autonomous and Electric Vehicles: Austria, Germany, South Korea and USA. In CHI '17: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Denver, AT, US, 2017 (under review).Google Scholar
- William Payre, Julien Cestac, and Patricia Delhomme. 2014. Intention to use a fully automated car: Attitudes and a priori acceptability. Transportation research part F: traffic psychology and behaviour 27 (2014), 252--263. Google ScholarCross Ref
- William Payre, Julien Cestac, and Patricia Delhomme. 2015. Fully Automated Driving Impact of Trust and Practice on Manual Control Recovery. Human Factors: The Journal of the Human Factors and Ergonomics Society (2015), 0018720815612319.Google Scholar
- Jonathan Posner, James A Russell, and Bradley S Peterson. 2005. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and psychopathology 17, 03 (2005), 715--734.Google Scholar
- Christina Rödel, Susanne Stadler, Alexander Meschtscherjakov, and Manfred Tscheligi. 2014. Towards Autonomous Cars: The Effect of Autonomy Levels on Acceptance and User Experience. In Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 1--8. Google ScholarDigital Library
- James A Russell, Anna Weiss, and Gerald A Mendelsohn. 1989. Affect grid: a single-item scale of pleasure and arousal. Journal of personality and social psychology 57, 3 (1989), 493.Google ScholarCross Ref
- Brandon Schoettle and Michael Sivak. 2014. A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia. Technical Report.Google Scholar
- Serge Thill, Maria Riveiro, and Maria Nilsson. 2015. Perceived intelligence as a factor in (semi-) autonomous vehicle UX. In " Experiencing Autonomous Vehicles: Crossing the Boundaries between a Drive and a Ride" workshop in conjunction with CHI2015.Google Scholar
- Viswanath Venkatesh, Michael G Morris, Gordon B Davis, and Fred D Davis. 2003. User acceptance of information technology: Toward a unified view. MIS quarterly (2003), 425--478.Google Scholar
- David Watson, Lee A Clark, and Auke Tellegen. 1988. Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of personality and social psychology 54, 6 (1988), 1063.Google ScholarCross Ref
- John D Watt. 1991. Effect of boredom proneness on time perception. Psychological Reports 69, 1 (1991), 323--327. Google ScholarCross Ref
- Automated Driving System, Male, or Female Driver: Who'd You Prefer? Comparative Analysis of Passengers' Mental Conditions, Emotional States & Qualitative Feedback
Recommendations
Why do you like to drive automated?: a context-dependent analysis of highly automated driving to elaborate requirements for intelligent user interfaces
IUI '19: Proceedings of the 24th International Conference on Intelligent User InterfacesTechnology acceptance is a critical factor influencing the adoption of automated vehicles. Consequently, manufacturers feel obliged to design automated driving systems in a way to account for negative effects of automation on user experience. Recent ...
How to Display Vehicle Information to Users of Automated Vehicles When Conducting Non-Driving-Related Activities
MHCIAutomated vehicles (AVs) are expected to enable users to engage in non-driving-related activities (NDRAs). However, users do not easily trust an automated vehicle which poses new challenges for automotive human-machine interfaces (HMIs). Over-presenting ...
Toward overtrust-free advanced driver assistance systems
Special Issue on Human-automation CoagencyAvoiding human overtrust in machines is a vital issue to establish a socially acceptable advanced driver assistance system (ADAS). However, research has not clarified the effective way of designing an ADAS to prevent driver overtrust in the system. It ...
Comments