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Questionnaire for "Automated Ride Comfort Assessment (ARCA)"

Authors:
Claus Marberger at Bosch
Claus Marberger
Hanna Otto at Bosch
Hanna Otto
Michael Schulz at Bosch
Michael Schulz
Philipp Alt at Bosch
Philipp Alt
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Abstract

This short questionnaire addresses motion-related aspects of ride comfort in automated vehicles. Is was developed and applied in several user studies in the publicly-funded project RUMBA (https://projekt-rumba.de/en/). A German version is also available for download.
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ARCA_e
n.pdf
Content uploaded by Claus Marberger
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All content in this area was uploaded by Claus Marberger on Jul 07, 2022
Content may be subject to copyright.
Questionnaire for subjective assessment of ride comfort in automated driving:
“Automated Ride Comfort Assessment (ARCA)”
Authors: Claus Marberger, Dr. Hanna Otto, Michael Schulz, Dr. Philipp Alt, Stefanie Horn
Affiliation: Robert Bosch GmbH
1 Background information
• The questionnaire addresses aspects of ride comfort in automated vehicles that is related to the design of the
automated vehicle motion. It deliberately excludes aspects of ride comfort that are determined by character-
istics of vehicle suspension, seating ergonomics, design of interior space, air quality etc.
• The questionnaire compiles several relevant physical and mental aspects of comfort/well-being on the basis
of a literature research conducted in AP1 of the RUMBA project (https://projekt-rumba.de/en/). Apart from
„classical“ criteria of ride comfort, the questionnaire also includes aspects of general user experience and
technology acceptance.
• Purpose of the tool: post-hoc assessment of a past drive or section of a drive; no explicit focus on single
driving situations.
• No assessment of technical parameters (like brake timing, deceleration dynamics, …), instead a clear focus
on subjective experience was chosen.
• 1 “direct”-item per factor and consistent format across all items keeps complexity low and allows multiple
usage of the tool within one trial.
• Response format does not only address discomfort (from „negative“ to “neutral”), but the complete spectrum
from discomfort to comfort (from „negative“ – red, to „neutral“ – white to „positive“ – green). This reflects the
underlying mental model of comfort. In this model comfort is a conscious experience of well-being, when
relevant physical and mental aspects surpass the expected level in a positive way. Conversely, discomfort
results, when these aspects fall short in relation to the expected level (cf. Carsten& Martens, 2019).
• In case single items do not fit to the conditions of the study they should be removed beforehand.
• The analysis should be done on single item level. In order to assess the overall comfort level of a past auto-
mated drive, the last two items can be used. Calculation of total scores is not foreseen and not recommended.
2 Current version of the questionnaire (2022/05/18)
Figure 1: Overview of underlying factors (left; not part of the questionnaire) and single items including response choices (right).
Sense of safety As a user of this system, I felt …. unsafe ◌ ◌ ◌ ◌ ◌ ◌ ◌ safe.
Naturalness Vehicle control appeared …. unnatural ◌ ◌ ◌ ◌ ◌ ◌ ◌ natural.
Cooperativity Vehicle behavior appeared … towards other road users. unfriendly ◌ ◌ ◌ ◌ ◌ ◌ ◌ friendly
Transparency Decisions for lane choice were … to me. not transparent ◌ ◌ ◌ ◌ ◌ ◌ ◌ transparent
Feeling of control Automated driving felt like ... control. losing ◌ ◌ ◌ ◌ ◌ ◌ ◌ gaining
Travel progress I had the impression to travel …. inefficiently ◌ ◌ ◌ ◌ ◌ ◌ ◌ efficiently.
Workload Automated driving made me feel … stressed ◌ ◌ ◌ ◌ ◌ ◌ ◌ relaxed.
Social environment Other road users probably think … about my vehicle's behavior. negative ◌ ◌ ◌ ◌ ◌ ◌ ◌ positive
Predictability I could … predict the vehicle's behavior. hardly ◌ ◌ ◌ ◌ ◌ ◌ ◌ easily
System trust My feeling of trust towards this system is … low ◌ ◌ ◌ ◌ ◌ ◌ ◌ high.
Interference with NDRT The driving behavior made it … to work with the mobile device. difficult ◌ ◌ ◌ ◌ ◌ ◌ ◌ easy
G-Forces (braking) G-forces due to braking were … inappropriate ◌ ◌ ◌ ◌ ◌ ◌ ◌ appropriate.
G-Forces (accelerating) G-forces due to acceleration were … inappropriate ◌ ◌ ◌ ◌ ◌ ◌ ◌ appropriate.
G-Forces (curves) G-forces in curves/turns were … inappropriate ◌ ◌ ◌ ◌ ◌ ◌ ◌ appropriate.
G-Forces (lane change) G-forces during lane changes were … inappropriate ◌ ◌ ◌ ◌ ◌ ◌ ◌ appropriate.
Fatigue My body feels physically … fatigued ◌ ◌ ◌ ◌ ◌ ◌ ◌ recovered.
Motion sickness Concerning motion sickness, I feel sick ◌ ◌ ◌ ◌ ◌ ◌ ◌ well.
General comfort All in all, the automated ride was … uncomfortable ◌ ◌ ◌ ◌ ◌ ◌ ◌ comfortable.
General drivign style I am … with the way the automation controlled the vehicle. unhappy ◌ ◌ ◌ ◌ ◌ ◌ ◌ happy
Ride comfort in automated driving
General
Psychological aspects
Physical aspects
3 Underlying literature
• Bellem, H. (2017). Analysis of Driving Style Preference in Automated Driving. Dissertation. Technische Uni-
versität Chemnitz, Chemnitz.
• Bellem, H., Schönenberg, T., Krems, J. F., & Schrauf, M. (2016). Objective metrics of comfort: Developing a
driving style for highly automated vehicles. In: Transportation Research Part F: Traffic Psychology and Be-
haviour 41, S. 45–54.
• Carsten, O., & Martens, M. H. (2019). How can humans understand their automated cars? HMI principles,
problems and solutions. Cognition, Technology & Work, 21(1), 3-20.
• Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 10-
114.
• Da Silva, M. C. G. (2002). Measurements of comfort in vehicles. In: Measurement Science and Technology
13 (6)
• Dovgan, E., Tušar, T., Javorski, M., & Filipič, B. (2012). Discovering comfortable driving strategies using sim-
ulation-based multiobjective optimization. In: Informatica 36 (3).
• Ellinghaus, D. & Schlag, B. (2001). Beifahrer. Eine Untersuchung über die psychologischen und soziologi-
schen Aspekte des Zusammenspiels von Fahrer und Beifahrer. Köln, Hannover.
• Engelbrecht, A. (2013). Fahrkomfort und Fahrspaß bei Einsatz von Fahrerassistenzsystemen. disserta Verlag.
• Elbanhawi, M., Simic, M., & Jazar, R. (2015). In the passenger seat: investigating ride comfort measures in
autonomous cars. IEEE Intelligent transportation systems magazine, 7(3), 4-17.
• Festner, M. (2019). Objektivierte Bewertung des Fahrstils auf Basis der Komfortwahrnehmung bei hochauto-
matisiertem Fahren in Abhängigkeit fahrfremder Tätigkeiten: Grundlegende Zusammenhänge zur komfortori-
entierten Auslegung eines hochautomatisierten Fahrstils (Doctoral dissertation, Universität Duisburg-Essen).
• Flemisch, F., Schieben, A., Schoemig, N., Strauss, M., Lueke, S., & Heyden, A. (2011, July). Design of human
computer interfaces for highly automated vehicles in the EU-Project HAVEit. In International Conference on
Universal Access in Human-Computer Interaction (pp. 270-279). Springer, Berlin, Heidelberg.
• Hartwich, F., Beggiato, M., Dettmann, A., & Krems, J. F. (2015). Customized Automated Driving Styles for
Younger and Older Drivers. VDI Bericht 2264, 271-283.
• Hassenzahl, M. (2007). The hedonic/pragmatic model of user experience. Towards a UX manifesto 10.
• Lange, A., Maas, M., Albert, M., Siedersberger, K.-H., & Bengler, K. (2014). Automatisiertes Fahren - So
komfortabel wir möglich, so dynamisch wie nötig. Vestibuläre Zustandsrückmeldung beim automatisierten
Fahren. In: VDI (Hg.): 30. VDI/VW-Gemeinschafts-tagung. Fahrerassistenz und Integrierte Sicherheit. Wolfs-
burg, 14.-15.10.2014. VDI/VW: VDI Verlag GmbH, S. 215–228.
• Peng, C., Merat, N., Romano, R., Hajiseyedjavadi, F., Paschalidis, E., Wei, C., ... & Boer, E. (2021). Drivers’
Evaluation of Different Automated Driving Styles: Is It Both Comfortable and Natural?
• Schönhammer, R. (1992). Zur Psychologie der Beifahrersituation. In: Deutschen Gesellschaft für Psychologie
(Hg.): Bericht über 38. Kongreß der Deutschen Gesellschaft für Psychologie. Trier. Göttingen: Hogrefe, S.
321–322.
• Sarter, N. B., Woods, D. D., & Billings, C. E. (1997). Automation surprises. Handbook of human factors and
ergonomics, 2, 1926-1943.
• Summala, H. (2007). Towards understanding motivational and emotional factors in driver behaviour: Comfort
through satisficing. In Modelling driver behaviour in automotive environments (pp. 189-207). Springer, London.

Supplementary resource (1)

ARCA_de.pdf
Data
July 2022
Claus Marberger · Hanna Otto · Michael Schulz · Philipp Alt · Stefanie Horn
ResearchGate has not been able to resolve any citations for this publication.
How can humans understand their automated cars? HMI principles, problems and solutions
Article
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  • Feb 2019
  • Cognit Tech Work
As long as vehicles do not provide full automation, the design and function of the Human Machine Interface (HMI) is crucial for ensuring that the human “driver” and the vehicle-based automated systems collaborate in a safe manner. When the driver is decoupled from active control, the design of the HMI becomes even more critical. Without mutual understanding, the two agents (human and vehicle) will fail to accurately comprehend each other’s intentions and actions. This paper proposes a set of design principles for in-vehicle HMI and reviews some current HMI designs in the light of those principles. We argue that in many respects, the current designs fall short of best practice and have the potential to confuse the driver. This can lead to a mismatch between the operation of the automation in the light of the current external situation and the driver’s awareness of how well the automation is currently handling that situation. A model to illustrate how the various principles are interrelated is proposed. Finally, recommendations are made on how, building on each principle, HMI design solutions can be adopted to address these challenges.
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UTAUT and UTAUT 2: A Review and Agenda for Future Research
Article
Full-text available
  • Sep 2012
This study reviews the most recent literature on UTAUT (Unified Theory of Acceptance, and Use of Technology) and UTAUT 2(Unified Theory of Acceptance, and Use of Technology) 2 by focusing on the findings and recommended future research. The papers, proceedings and dissertations included in the analysis were identified technology acceptance as the focus of their studies. This search was supplemented various websites which host scientific journals such as Emerald, Science Direct and Google Scholar. The initial search produced 65 papers. The studies examined works which employed UTAUT and UTAUT 2 by focusing on findings on the core constructs of UTAUT to predict Behavioral Intentions. The results confirmed previous studies that the four constructs of UTAUT contributed to Behavioral Intention even though PE seemed to be the most significant contributors. Findings also suggest UTAUT 2 has been more explanatory and list the suggestions for future works. The immediate implications are for researchers who wish to examine behavioral intentions, and managers who wish to ensure the acceptance and use of a new system or technology. This study bears a number of limitations. Number of papers examined is one of them. It would be more accurate to increase the number of paper examined. The other limitation is the ability to draw a statistical conclusion each research examined. This is due to a great variety of research topics, methods, constructs and contexts.
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Towards Understanding Motivational and Emotional Factors in Driver Behaviour: Comfort Through Satisficing
Chapter
Full-text available
  • Jan 2007
The early’ skill models’ of driver behaviour and safety posited that the safety of a driver is mainly determined by the level of his or her perceptual and motor skills in relation to the task demands: a crash — a failure in driver performance —occurs when task demands exceed driver capabilities (e.g., Blumenthal, 1968). Consequently, improving driver skills and reducing task demands would make traffic safer. Obviously, however, this early concept was too simple. A good piece of counterevidence, among others, came from Williams and O’Neill (1974), who showed that classified U.S. race drivers have more crashes per exposure than average drivers — and also more speeding tickets. At least the advanced skills which make those drivers competitive in race track did not save them from crashes, as they obviously traded skills for speed on ordinary roads, too. Theorists and road safety people indeed forgot that driving is a self-paced task and drivers themselves do determine their task demands to a large extent (Näätänen and Summala, 1974). The behavioural adaptation concept is now one of basic tenets in traffic psychology, here defined by Summala(1996): ‘the driver is inclined to react to changes in the traffic system, whether they be in the vehicle, in the road environment, in road and weather conditions, or in his/her own skills or states, and that this reaction occurs in accordance with his/her motives’. 11.2 Emotional Tension and 'Risk Monitor' 11.3 Safety Margins and Safety Zone 11.4 Available Time, Workload and Multilevel Task Control 11.5 Safety Margins, Affordances and Skills 11.6 Towards Unifying Emotional Concepts in Routine Driving 11.6.1 Safety Margins - To Control and Survive 11.6.2 Vehicle/Road System - To Provide Smooth and Comfortable Travel 11.6.3 Rule Following - To Avoid Sanctions 11.6.4 Good (or Expected) Progress of Trip –Mobility and Pace/Progress 11.7 Comfort Through Satisficing 11.8 'Go to the Road': Need of On-Road Research
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Objektivierte Bewertung des Fahrstils auf Basis der Komfortwahrnehmung bei hochautomatisiertem Fahren in Abhängigkeit fahrfremder Tätigkeiten: Grundlegende Zusammenhänge zur komfortorientierten Auslegung eines hochautomatisierten Fahrstils (Doctoral dissertation
  • Jan 2019
  • M Festner
• Festner, M. (2019). Objektivierte Bewertung des Fahrstils auf Basis der Komfortwahrnehmung bei hochautomatisiertem Fahren in Abhängigkeit fahrfremder Tätigkeiten: Grundlegende Zusammenhänge zur komfortorientierten Auslegung eines hochautomatisierten Fahrstils (Doctoral dissertation, Universität Duisburg-Essen).
Design of human computer interfaces for highly automated vehicles in the EU-Project HAVEit
  • Jul 2011
  • 271-283
  • F Flemisch
  • A Schieben
  • N Schoemig
  • M Strauss
  • S Lueke
  • A Heyden
  • F Hartwich
  • M Beggiato
  • A Dettmann
  • J F Krems
• Flemisch, F., Schieben, A., Schoemig, N., Strauss, M., Lueke, S., & Heyden, A. (2011, July). Design of human computer interfaces for highly automated vehicles in the EU-Project HAVEit. In International Conference on Universal Access in Human-Computer Interaction (pp. 270-279). Springer, Berlin, Heidelberg. • Hartwich, F., Beggiato, M., Dettmann, A., & Krems, J. F. (2015). Customized Automated Driving Styles for Younger and Older Drivers. VDI Bericht 2264, 271-283.
The hedonic/pragmatic model of user experience. Towards a UX manifesto 10
  • Jan 2007
  • M Hassenzahl
• Hassenzahl, M. (2007). The hedonic/pragmatic model of user experience. Towards a UX manifesto 10.
Automatisiertes Fahren -So komfortabel wir möglich, so dynamisch wie nötig. Vestibuläre Zustandsrückmeldung beim automatisierten Fahren. In: VDI (Hg.): 30. VDI/VW-Gemeinschafts-tagung
  • Jan 2014
  • 215-228
  • A Lange
  • M Maas
  • M Albert
  • K.-H Siedersberger
  • K Bengler
• Lange, A., Maas, M., Albert, M., Siedersberger, K.-H., & Bengler, K. (2014). Automatisiertes Fahren -So komfortabel wir möglich, so dynamisch wie nötig. Vestibuläre Zustandsrückmeldung beim automatisierten Fahren. In: VDI (Hg.): 30. VDI/VW-Gemeinschafts-tagung. Fahrerassistenz und Integrierte Sicherheit. Wolfsburg, 14.-15.10.2014. VDI/VW: VDI Verlag GmbH, S. 215-228.
Drivers' Evaluation of Different Automated Driving Styles: Is It Both Comfortable and Natural?
  • Jan 1992
  • 321-322
  • C Peng
  • N Merat
  • R Romano
  • F Hajiseyedjavadi
  • E Paschalidis
  • C Wei
  • . . Boer
  • E Schönhammer
• Peng, C., Merat, N., Romano, R., Hajiseyedjavadi, F., Paschalidis, E., Wei, C.,... & Boer, E. (2021). Drivers' Evaluation of Different Automated Driving Styles: Is It Both Comfortable and Natural? • Schönhammer, R. (1992). Zur Psychologie der Beifahrersituation. In: Deutschen Gesellschaft für Psychologie (Hg.): Bericht über 38. Kongreß der Deutschen Gesellschaft für Psychologie. Trier. Göttingen: Hogrefe, S. 321-322.