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Fly Away: Evaluating the Impact of Motion Fidelity on Optimized User Interface Design via Bayesian Optimization in Automated Urban Air Mobility Simulations

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Adding physical motion to a VR air-taxi simulation makes passengers report lower trust, understanding, and acceptance of the interface.

desk verdict A genuine first combining MOBO with a motion-fidelity manipulation in simulated air taxis, but the headline Bayes factors rest on non-independent Pareto-front observations and a group comparison partly confounded by the optimized UIs themselves. read the letter →

arxiv 2501.11829 v2 pith:MJAFMUYX submitted 2025-01-21 cs.HC

Luca-Maxim Meinhardt , Clara Schramm , Pascal Jansen , Mark Colley , Enrico Rukzio This is my paper · ORCID
classification cs.HC
keywords urbanairmobilityvirtualrealitymotionfidelitymulti-objectiveBayesianoptimizationtrustinautomationtaxiinterfaceParetofronthuman-in-the-loop
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the physical motion fidelity of a simulator changes what passengers feel and what interface they prefer in automated air taxis. The authors ran 40 participants through a VR air-taxi flight, half with a three-degree-of-freedom motion chair and half without, while a Bayesian optimizer repeatedly adjusted twelve interface parameters to maximize six subjective goals. They report strong-to-extreme Bayesian evidence that adding motion lowers trust, understanding, and acceptance, and moderate evidence it lowers perceived safety and aesthetics. The optimized interfaces themselves differed mainly in two parameters: passengers who felt motion preferred smaller chevrons on other air taxis and wanted boundary boxes around them, while passengers without motion preferred larger chevrons and no boxes. If this holds, UAM researchers need motion cues when they want realistic absolute ratings, but simple VR may suffice for comparing interface variants.

What carries the argument

The central mechanism is Multi-Objective Bayesian Optimization (MOBO), a human-in-the-loop procedure that maps twelve design parameters (trajectory lengths, transparencies, chevron sizes, map and box toggles) to six subjective objectives (trust, perceived safety, mental demand, understanding, acceptance, and aesthetics), and proposes the design expected to improve the Pareto trade-off most. Each participant went through 30 iterations, and only the non-dominated Pareto-optimal designs were analyzed. The comparison across motion conditions then rests on independent Bayesian t-tests whose Bayes factors quantify evidence for difference versus equality.

What would settle it

Run the same procedure with each participant rating one randomly assigned UI design exactly once, with no adaptive optimization, and compare motion versus no-motion; if trust ratings no longer separate the groups, the reported effect depends on the adaptive loop rather than on motion itself. Alternatively, re-analyze the Pareto-front ratings with a model that treats participant as a random effect; if the trust Bayes factor falls below the extreme threshold, the independence assumption carried the result.

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Extended reading notes

Core claim

The paper claims that adding physical motion cues to a VR air-taxi simulation does not simply make the experience feel more real; it changes the user's subjective response to the interface. On Pareto-optimal designs, the motion group rated trust lower (BF = 3970.14), understanding lower (BF = 56.85), and acceptance lower (BF = 67.92) than the no-motion group. The optimized interfaces differed between the groups mainly in two ways: without motion, participants preferred larger chevrons marking other air taxis and no boundary boxes; with motion, they preferred smaller chevrons and boundary boxes around other air taxis. Other design parameters showed no strong group difference, and the authors interpret the large spread of Pareto-front designs as evidence against a single best air-taxi interface.

Load-bearing premise

The headline evidence counts each Pareto-optimal rating as an independent data point, but all ratings from one participant came from a single session in which the interface kept adapting to that person's own preferences.

Editorial extensions

If this is right

  • Studies that need realistic absolute ratings of air-taxi interfaces should include motion cues, because no-motion ratings overstate trust, understanding, and acceptance.
  • Comparative UI studies can probably use VR or even monitor setups without motion, since motion changed only two of twelve optimized design parameters.
  • Boundary boxes around other air taxis should be included in air-taxi simulations regardless of motion, because the motion condition preferred them.
  • A one-size-fits-all optimized interface is unlikely to satisfy passengers; personalization appears necessary.
  • Reducing the objective set from six to fewer dimensions (for instance, dropping understanding because it tracks trust, or aesthetics because it tracks acceptance) would make future optimization simpler.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • One plausible reading, not tested by the authors: the motion-driven drop in trust may be a familiarity effect, since no participant had flown in an air taxi; repeated exposure to the motion condition could attenuate the gap.
  • The comparison pools Pareto-front points across participants even though each participant's optimization loop personalized the interface, so the group-level optimized UI is a statistical construct; a design that is best for the average may not be best for any given passenger.
  • A testable extension would vary motion amplitude (none, 3-DoF, 6-DoF) to see whether trust decreases monotonically with fidelity or shows a threshold.
  • Because boundary boxes appeared compensatory in the motion condition, a design implication is that trust-reducing contexts may call for reassurance elements that are unnecessary in calmer conditions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper reports a between-subjects VR study (N=40, n=20 per group) that uses multi-objective Bayesian optimization (MOBO) to personalize a 12-parameter air-taxi interface for each participant, with one group experiencing 3-DoF motion cues via a chair and the other VR only. The authors analyze only the Pareto-front designs extracted per participant, then use independent Bayesian t-tests to compare the two groups on six subjective objectives and on each design parameter. They report strong to extreme evidence that motion fidelity lowers trust, understanding, and acceptance, and strong/extreme evidence of group differences in other-chevron size and boundary-box visibility, while interpreting most remaining parameter differences as evidence for personalization rather than group effects.

Significance. If the central claim is supported, the result is practically important for UAM simulation methodology: it would indicate that motion fidelity systematically lowers subjective ratings and that such fidelity should be considered when generalizing from VR-only studies to real flights. The human-in-the-loop MOBO pipeline is a novel and timely methodological contribution, and the paper is transparent about many of its design choices. However, the strength of the central claim rests on a statistical analysis whose assumptions about independence are not met, and the group comparison is confounded by the very design parameters the optimization produced. The evidentiary basis therefore needs reworking before the headline conclusions can be accepted.

major comments (3)
  1. [3.7.1 and 3.7.4] The central claim of strong to extreme evidence (BF=3970.14 for trust, 67.92 for acceptance, 56.85 for understanding) rests on independent Bayesian t-tests applied to pooled Pareto-front observations. Section 3.7.1 reports n=48 and n=42 Pareto points from only 20 participants per group, meaning each participant contributes multiple observations from a single 30-run adaptive loop in which the UI was updated based on that participant's own ratings. These observations are nested within participants and serially correlated, so the effective sample size is smaller than the reported number of Pareto points. Treating them as independent can substantially inflate Bayes factors. A participant-level summary (e.g., one value per participant, such as the mean or median over that participant's Pareto front) or a multilevel Bayes factor is required before the 'strong to extreme evidence' language in Section 4.1 can be taken at face value.
  2. [3.7.3 and 4.1] The group comparison of questionnaire ratings is confounded with the optimized UI design. Table 1 reports strong evidence of a difference in Other Chevron Size and extreme evidence of a difference in Boundary Box between the motion and no-motion groups. Because the participants in the two groups rated different UI configurations, the observed differences in trust, understanding, and acceptance are not attributable solely to motion fidelity; they could partly reflect the differing chevron sizes or boundary-box visibility. This is especially important because Section 4.2 argues that boundary boxes were preferred in the motion condition as a compensatory response to reduced trust. The paper should either acknowledge and discuss this confound explicitly or provide an analysis that controls for the design configuration (for example, by comparing ratings on a common, non-personalized design).
  3. [Table 1 and Section 3.7.3] The extreme Bayes factor for Boundary Box (BF=32473.69) is not robust. Boundary Box is a binary parameter defined by a 0.5 threshold on a continuous MOBO value, and the no-motion median is 0.45 with an IQR of (0.35, 0.63), so the median lies on one side of the threshold while the IQR crosses it. The reported extreme evidence is therefore heavily dependent on the arbitrary threshold and on pooling multiple Pareto points per participant. A sensitivity analysis around the threshold, or a model that treats the binary parameter directly rather than thresholding the pooled continuous values, is needed before this parameter can be claimed as an extreme group difference.
minor comments (5)
  1. [Abstract and Section 4.2] The abstract states that 'minimal evidence was found for differences or equality in the optimized interface designs,' but Table 1 reports strong evidence for Other Chevron Size and extreme evidence for Boundary Box; the wording should be aligned with the actual Bayes factors.
  2. [Section 3.3] The descriptions of the binary parameters contain a copy-paste error: for 'Additional Information on Display', the parenthetical says '(< 0.5 no boundary box' instead of 'no additional information'; the same style of error appears in the description of the 'Map Display' and 'Boundary Box' parameters.
  3. [Section 3.7.3] The text reports means and SDs for the two groups, while Table 1 reports medians and IQRs; please clarify which summary is used for the Bayesian t-tests and keep the presentation consistent.
  4. [Figure 6(a)] The trust panel is annotated with 'BF > 100' rather than the exact reported value of 3970.14; using the exact value (or a consistent descriptive label) would improve accuracy.
  5. [General] No data or analysis code availability statement is provided. Given that the analysis is central to the claims, making the de-identified data and analysis scripts available would strengthen reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims rest on measured group comparisons, not on outputs fed back as inputs.

full rationale

This paper is an empirical between-subjects user study, not a derivation. The central claim that motion fidelity reduces trust, understanding, and acceptance comes from Bayesian t-tests comparing questionnaire ratings of Pareto-optimal UI designs from the motion group (n=48 Pareto points) and the no-motion group (n=42 Pareto points). The MOBO procedure fits UI design parameters to each participant's own subjective ratings during 30 optimization runs, and the paper explicitly treats the resulting designs as outcomes to be compared, not as predictions derived from a theory. No equation in the paper defines a reported quantity in terms of the fitted parameters such that the result is forced by construction. The design-parameter differences (e.g., chevron size, boundary box) are also measured comparisons of Pareto-selected values, not predictions from the fitted model. The self-citations to prior work on path visualizations and information needs supply background and design choices (e.g., including other air taxis, chevron visualizations) but do not carry the central evidence claim; the motion-fidelity effect is established by newly collected participant ratings. The post-hoc interpretations of why boundary boxes appear in the motion condition are explanatory narratives, not circular derivations. The weakness noted in the reader's take, that pooled Pareto-front observations are nested within participants and serially correlated, is a legitimate statistical validity concern that could inflate Bayes factors, but it is not a circularity concern: it affects the evidentiary strength of a measurement, not whether an output reduces to an input. No self-citation chain, imported uniqueness theorem, or ansatz-smuggling citation is load-bearing for the main result. No circular steps were found, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

This is an empirical HCI study, so the ledger records methodological and statistical assumptions rather than mathematical axioms or new physical entities. The central claims depend on the validity of the Bayes factor analysis, the motion chair as a stand-in for real flight, the optimization's convergence, and the questionnaire measures.

free parameters (2)
  • Binary parameter threshold = 0.5
    Hand-set threshold for converting continuous MOBO outputs into visible or invisible boolean UI elements. It directly determines whether the no-motion group's median boundary box value of 0.45 is interpreted as 'no boundary box', which anchors the strong difference claim for that parameter.
  • Number of optimization runs = 30
    Hand-set as five Sobol seeding runs plus 25 exploitation runs. The paper notes in Appendix A that objectives were still improving at run 30, so the computed Pareto front may not represent the true optimum, affecting all subsequent group comparisons.
assumptions (5)
  • standard math Bayes factor interpretation thresholds
    The labels 'anecdotal', 'moderate', 'strong', and 'extreme' for both differences and equality are assigned using the conventional Bayes factor bands from Lee and Wagenmakers (2013), invoked in section 3.7.1 and Figure 5.
  • domain assumption Independence of Pareto-front observations
    The Bayesian t-tests in section 3.7.4 treat Pareto-optimal ratings as independent observations. In reality each participant contributes multiple Pareto points from a single personalized 30-run optimization, so the observations are nested and not independent.
  • domain assumption 3-DoF motion chair represents real air taxi motion
    The YAW VR chair reproduces the simulated air taxi's yaw, pitch, and roll, but the paper does not validate this against actual air taxi motion. Section 4.1 even notes that participants had no real air taxi experience, which weakens the claim that the motion condition reflects real-world responses.
  • domain assumption Convergence of MOBO within 30 runs
    The analysis assumes the computed Pareto front approximates the best achievable designs. Appendix A shows the averaged objectives were still increasing at run 30, indicating the optimization may not have converged, which would bias the comparison of optimized design parameters.
  • domain assumption Subjective Likert ratings measure the intended constructs
    Trust, understanding, mental demand, and perceived safety are measured with established questionnaires, but the aesthetics item is self-created and not validated, as the paper admits in section 4.4. The acceptance items are inspired by, not identical to, the van der Laan scale.

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Cite this review

Pith. "Pith review of Fly Away: Evaluating the Impact of Motion Fidelity on Optimized User Interface Design via Bayesian Optimization in Automated Urban Air Mobility Simulations." pith.science (2026). https://pith.science/paper/MJAFMUYX

@misc{pith2026250111829,
  author       = {Pith},
  title        = {Pith review of: Fly Away: Evaluating the Impact of Motion Fidelity on Optimized User Interface Design via Bayesian Optimization in Automated Urban Air Mobility Simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MJAFMUYX}},
  note         = {Machine review of arXiv:2501.11829}
}
read the original abstract

Automated Urban Air Mobility (UAM) can improve passenger transportation and reduce congestion, but its success depends on passenger trust. While initial research addresses passengers' information needs, questions remain about how to simulate air taxi flights and how these simulations impact users and interface requirements. We conducted a between-subjects study (N=40), examining the influence of motion fidelity in Virtual-Reality-simulated air taxi flights on user effects and interface design. Our study compared simulations with and without motion cues using a 3-Degrees-of-Freedom motion chair. Optimizing the interface design across six objectives, such as trust and mental demand, we used multi-objective Bayesian optimization to determine the most effective design trade-offs. Our results indicate that motion fidelity decreases users' trust, understanding, and acceptance, highlighting the need to consider motion fidelity in future UAM studies to approach realism. However, minimal evidence was found for differences or equality in the optimized interface designs, suggesting personalized interface designs.

Figures

Figures reproduced from arXiv: 2501.11829 by the authors.

Figure 1
Figure 1. Overview of the user study setup and optimization process. (Left) N=40 participants were divided into two groups: [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Design parameters to be optimized. The figure shows the values of 0, 0.5 and 1 for each design parameter [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Optimization Process of the averaged normalized [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Exemplary optimized design parameters of Participant 40 (no Motion) and Participant 1 (with Motion). The concrete [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Comparison of the design parameters for both groups, using a Bayesian t-test. For both groups, the IQR is plotted. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Rating for the subjective questionnaires comparing both groups: no Motion and with Motion of all Pareto optimal [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Correlation of all Objectives shows that most of [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Optimization process of each objective during the 30 runs [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

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Reference graph

Works this paper leans on

86 extracted references · 36 canonical work pages · cited by 1 Pith paper

  1. [1]

    European Union Aviation Safety Agency. 2021. Study on the societal acceptance of Urban Air Mobility in Europe. European Union Aviation Safety Agency. Retrieved March 11, 2022 from https://www.easa.europa.eu/sites/default/files/dfu/uam-full- report.pdf

    2021

  2. [2]

    Gautam Agrawal, Christina Bloebaum, Kemper Lewis, Kevin Chugh, Chen-Hung Huang, and Sumeet Parashar. 2004. Intuitive Visualization of Pareto Frontier for Multiobjective Optimization in n-Dimensional Performance Space. In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference . American Institute of Aeronautics and Astronautics, Albany, New...

    work page doi:10.2514/6.2004- 2004

  3. [3]

    Christelle Al Haddad, Emmanouil Chaniotakis, Anna Straubinger, Kay Plötner, and Constantinos Antoniou. 2020. Factors affecting the adoption and use of urban air mobility. Transportation Research Part A: Policy and Practice 132 (2020), 696–712. doi:10.1016/j.tra.2019.12.020

    doi:10.1016/j.tra.2019.12.020 2020

  4. [4]

    Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy

    Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy. 2020. BoTorch: A Frame- work for Efficient Monte-Carlo Bayesian Optimization. In Advances in Neural Information Processing Systems 33 . http://arxiv.org/abs/1910.06403

    arXiv 2020

  5. [5]

    Keegan Bergman. 2018. Mixed-Reality and Traditional Flight Simulators for Urban Air Mobility Vehicles. In Proceedings of the Wisconsin Space Conference

    2018

  6. [6]

    Guillaume Bouyer, Amine Chellali, and Anatole Lécuyer. 2017. Inducing Self- Motion Sensations in Driving Simulators Using Force-Feedback and Haptic Mo- tion. In 2017 IEEE Virtual Reality (VR) . 84–90. doi:10.1109/VR.2017.7892234

    work page arXiv 2017

  7. [7]

    Eric Brochu, Tyson Brochu, and Nando De Freitas. 2010. A Bayesian interactive optimization approach to procedural animation design. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation . ACM, New York, NY, USA, 103–112

    2010

  8. [8]

    Cora, and Nando de Freitas

    Eric Brochu, Vlad M. Cora, and Nando de Freitas. 2010. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. http://arxiv.org/abs/1012. 2599 arXiv:1012.2599 [cs]

    arXiv 2010

Show all 86 references
  1. [9]

    Lucas Bruck, Bruce Haycock, and Ali Emadi. 2021. A Review of Driving Simula- tion Technology and Applications. IEEE Open Journal of Vehicular Technology 2 (2021), 1–16. doi:10.1109/OJVT.2020.3036582

    2021

  2. [10]

    Liwei Chan, Yi-Chi Liao, George B Mo, John J Dudley, Chun-Lien Cheng, Per Ola Kristensson, and Antti Oulasvirta. 2022. Investigating Positive and Negative Qual- ities of Human-in-the-Loop Optimization for Designing Interaction Techniques. In Proceedings of the 2022 CHI Confere...

    2022

  3. [11]

    Suyog Chandramouli, Yifan Zhu, and Antti Oulasvirta. 2023. Interactive Person- alization of Classifiers for Explainability Using Multi-Objective Bayesian Opti- mization. In Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (Limassol, Cypru...

    2023

  4. [12]

    L D Chen, Yiannis Papelis, Ginger Watson, and Dario Solis. 2001. NADS AT THE UNIVERSITY OF IOWA: A TOOL FOR DRIVING SAFETY RESEARCH. (2001), 14

    2001

  5. [13]

    Po-Wen Chiu and Christina Bloebaum. 2008. Hyper-Radial Visualization (HRV) for Decision-Making in Multi-Objective Optimization. In 46th AIAA Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics, Reno, Nevada. doi:10.2514/6.2008-907

    2008 doi

  6. [15]

    Mark Colley, Benjamin Eder, Jan Ole Rixen, and Enrico Rukzio. 2021. Effects of Semantic Segmentation Visualization on Trust, Situation Awareness, and Cognitive Load in Highly Automated Vehicles. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . ...

    2021

  7. [16]

    Mark Colley, Pascal Jansen, Enrico Rukzio, and Jan Gugenheimer. 2021. SwiVR- Car-Seat: Exploring Vehicle Motion Effects on Interaction Quality in Virtual Reality Automated Driving Using a Motorized Swivel Seat. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiqui...

    2021

  8. [17]

    Mark Colley, Luca-Maxim Meinhardt, Alexander Fassbender, Michael Rietzler, and Enrico Rukzio. 2023. Come Fly With Me - Investigating the Effects of Path Visualizations in Automated Urban Air Mobility. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., IMWUT 7, 2 (2023), 52...

    2023

  9. [19]

    Mark Colley, Max Rädler, Jonas Glimmann, and Enrico Rukzio. 2022. Effects of Scene Detection, Scene Prediction, and Maneuver Planning Visualizations on Trust, Situation Awareness, and Cognitive Load in Highly Automated Vehicles. Proc. ACM Interact. Mob. Wearable Ubiquitous Tec...

    2022 doi

  10. [20]

    Fabien Danieau, Julien Fleureau, Philippe Guillotel, Nicolas Mollet, Anatole Lécuyer, and Marc Christie. 2012. HapSeat: Producing Motion Sensation with Multiple Force-Feedback Devices Embedded in a Seat. In Proceedings of the 18th ACM Symposium on Virtual Reality Software and ...

    2012

  11. [21]

    Dudley, Jason T

    John J. Dudley, Jason T. Jacques, and Per Ola Kristensson. 2019. Crowdsourcing Interface Feature Design with Bayesian Optimization. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machi...

    2019

  12. [22]

    Mark Dunlop and John Levine. 2012. Multidimensional Pareto Optimization of Touchscreen Keyboards for Speed, Familiarity and Improved Spell Checking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Austin, Texas, USA)(CHI ’12). Association for Com...

    2012

  13. [23]

    Thomas Edwards and George Price. 2020. eVTOL Passenger Acceptance. 27 pages. https://ntrs.nasa.gov/citations/20200000532 Contractor Report

    2020

  14. [24]

    EHang. 2020. The future of Transportation: White Paper on Urban Air Mobility Systems. EHang. https://www.ehang.com/app/en/EHang%20White%20Paper% 20on%20Urban%20Air%20Mobility%20Systems.pdf

    2020

  15. [25]

    Faas, Andrea C

    Stefanie M. Faas, Andrea C. Kao, and Martin Baumann. 2020. A Longitudinal Video Study on Communicating Status and Intent for Self-Driving Vehicle – Pedestrian Interaction. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ...

    2020

  16. [26]

    Haiyan Fan and Marshall Scott Poole. 2006. What Is Personalization? Perspectives on the Design and Implementation of Personalization in Information Systems. Journal of Organizational Computing and Electronic Commerce 16, 3-4 (Jan. 2006), 179–202. doi:10.1080/10919392.2006.9681199

    2006

  17. [27]

    Loren Puchalla Fiore, Lane Phillips, Gerd Bruder, Victoria Interrante, and Frank Steinicke. 2012. Redirected Steering for Virtual Self-Motion Control with a Motorized Electric Wheelchair. doi:10.2312/EGVE/JVRC12/045-048 Artwork Size: 4 pages ISBN: 9783905674408 ISSN: 1727-530X...

    2012 doi

  18. [28]

    Flohr, Joseph Sebastian Valiyaveettil, Antonio Krüger, and Dieter P

    Lukas A. Flohr, Joseph Sebastian Valiyaveettil, Antonio Krüger, and Dieter P. Wallach. 2023. Prototyping Autonomous Vehicle Windshields with AR and Real-Time Object Detection Visualization: An On-Road Wizard-of-Oz Study. In Proceedings of the 2023 ACM Designing Interactive Sys...

    2023

  19. [29]

    Garrow, Aude Marzuoli, Satadru Roy, and Michel Bierlaire

    Julien Haan, Laurie A. Garrow, Aude Marzuoli, Satadru Roy, and Michel Bierlaire

  20. [30]

    Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Advances in psy- chology. Vol. 52. Elsevier, Amsterdam, The Netherlands, 139–183

    1988

  21. [31]

    Vincent Hayward, Jehangir Choksi, Gonzalo Lanvin, and Christophe Ramstein

  22. [33]

    Philipp Hock, Johannes Kraus, Marcel Walch, Nina Lang, and Martin Baumann

  23. [34]

    Kevin Anthony Hoff and Masooda Bashir. 2015. Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors 57, 3 (2015), 407–434

    2015

  24. [35]

    Dominik Janetzko and Bacem Kacem. 2024. What Do You Need? Information Requirements and Task Analysis of (Future) Advanced Air Mobility Pilots in the Emergency Medical Service. Aerospace 11, 3 (Feb. 2024), 197. doi:10.3390/ aerospace11030197

    2024

  25. [36]

    Christoph Albert Johns, João Marcelo Evangelista Belo, Anna Maria Feit, Clemens Nylandsted Klokmose, and Ken Pfeuffer. 2023. Towards Flexible and Robust User Interface Adaptations With Multiple Objectives. InProceedings of the 36th Annual ACM Symposium on User Interface Softwa...

    2023

  26. [37]

    Florian Kadner, Yannik Keller, and Constantin Rothkopf. 2021. AdaptiFont: Increasing Individuals’ Reading Speed with a Generative Font Model and Bayesian Optimization. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). ...

    2021 doi

  27. [38]

    Kennedy, Norman E

    Robert S. Kennedy, Norman E. Lane, Kevin S. Berbaum, and Michael G. Lilienthal

  28. [39]

    Gwangbin Kim, Dohyeon Yeo, Taewoo Jo, Daniela Rus, and SeungJun Kim. 2023. What and When to Explain?: On-road Evaluation of Explanations in Highly Automated Vehicles. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, 3 (Sept. 2023), 1–26. d...

    2023 doi

  29. [40]

    Jiwon Kim, Jihong Hwang, and Taezoon Park. 2020. Effect of Motion Cues on Simulator Sickness in a Flight Simulator. InVirtual, Augmented and Mixed Reality. Design and Interaction, Jessie Y. C. Chen and Gino Fragomeni (Eds.). Vol. 12190. Springer International Publishing, Cham,...

    2020 doi

  30. [41]

    Young Woo Kim, Cherin Lim, Seul Chan Lee, Sol Hee Yoon, and Yong Gu Ji. 2021. The 1st Workshop on User Experience in Urban Air Mobility: Design Considera- tions and Issues. In 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (L...

    2021

  31. [42]

    Moritz Körber. 2019. Theoretical Considerations and Development of a Question- naire to Measure Trust in Automation. In Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) , Sebastiano Bagnara, Riccardo Tartaglia, Sara Albolino, Thomas Alexa...

    2019

  32. [43]

    Oswald Kothgassner, A Felnhofer, N Hauk, E Kastenhofer, J Gomm, and I Krysprin-Exner. 2013. Technology Usage Inventory. https: //www.ffg.at/sites/default/files/allgemeine_downloads/thematische% 20programme/programmdokumente/tui_manual.pdf. Manual. Wien: ICARUS 17, 04 (2013), 9...

    2013

  33. [44]

    Yuki Koyama and Masataka Goto. 2022. BO as Assistant: Using Bayesian Op- timization for Asynchronously Generating Design Suggestions. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend, OR, USA) (UIST ’22). Association for Computing...

    2022

  34. [45]

    J. D. Lee and K. A. See. 2004. Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society 46, 1 (Jan. 2004), 50–80. doi:10.1518/hfes.46.1.50_30392

    2004 doi

  35. [46]

    Lee and E.J

    M.D. Lee and E.J. Wagenmakers. 2013. Bayesian Cognitive Modeling: A Practi- cal Course. Cambridge University Press. https://books.google.de/books?id= 50tkAgAAQBAJ

    2013

  36. [47]

    Yi-Chi Liao, John J Dudley, George B Mo, Chun-Lien Cheng, Liwei Chan, Antti Oulasvirta, and Per Ola Kristensson. 2023. Interaction Design With Multi- objective Bayesian Optimization. IEEE Pervasive Computing 22, 1 (2023), 29–38

    2023

  37. [48]

    Cherin Lim, Young Woo Kim, Yong Gu Ji, Solhee Yoon, and Seul Chan Lee. 2022. Is This Flight Headed Downtown? : User Experience Considerations for Urban Air Mobility. In CHI Conference on Human Factors in Computing Systems Extended Abstracts, Simone Barbosa, Cliff Lampe, Caroli...

    2022

  38. [49]

    Strybel, and Vernol Battiste

    Panadda Marayong, Praveen Shankar, Jessica Wei, Hanson Nguyen, Thomas Z. Strybel, and Vernol Battiste. 2020. Urban Air Mobility System Testbed using CAVE Virtual Reality Environment. In 2020 IEEE Aerospace Conference . IEEE, Big Sky, MT, USA, 1–7. doi:10.1109/AERO47225.2020.9172534

    2020

  39. [50]

    Marler and J.S

    R.T. Marler and J.S. Arora. 2004. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26, 6 (April 2004), 369–395. doi:10.1007/s00158-003-0368-6

    2004 doi

  40. [51]

    Michael Mccauley, Lawrence Hettinger, Thomas Sharkey, and John Sinacori. 1990. The effects of simulator visual-motion asynchrony on simulator induced sickness. In Flight Simulation Technologies Conference and Exhibit . American Institute of Aeronautics and Astronautics, Dayton...

    1990 doi

  41. [52]

    Luca-Maxim Meinhardt, Mark Colley, Alexander Faßbender, Michael Rietzler, and Enrico Rukzio. 2023. Up, Up and Away - Investigating Information Needs for Helicopter Pilots in Future Urban Air Mobility. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computi...

    2023

  42. [53]

    Luca-Maxim Meinhardt, Mark Colley, Marjana Tahmid, Max Rädler, and Enrico Rukzio. 2024. Wind of Change: Investigating Information Visualizations for Pas- sengers and Residents’ Perception of Automated Urban Air Mobility. Proceedings of the ACM on Interactive, Mobile, Wearable ...

    2024 doi

  43. [54]

    Luca-Maxim Meinhardt, Maximilian Rück, Julian Zähnle, Maryam Elhaidary, Mark Colley, Michael Rietzler, and Enrico Rukzio. 2024. Hey, What’s Going On? Conveying Traffic Information to People with Visual Impairments in Highly Automated Vehicles: Introducing OnBoard. Proc. ACM In...

    2024 doi

  44. [55]

    Olaf Mersmann. 2024. emoa: Evolutionary Multiobjective Optimization Algorithms. doi:10.32614/CRAN.package.emoa R package version 0.5-3

    2024 doi

  45. [56]

    George Mo, John Dudley, Liwei Chan, Yi-Chi Liao, Antti Oulasvirta, and Per Ola Kristensson. 2024. Cooperative Multi-Objective Bayesian Design Optimization. ACM Transactions on Interactive Intelligent Systems 14, 2 (June 2024), 1–28. doi:10. 1145/3657643

    2024

  46. [57]

    Navid Mohajer, Hamid Abdi, Kyle Nelson, and Saeid Nahavandi. 2015. Vehicle Motion Simulators, a Key Step towards Road Vehicle Dynamics Improvement. Vehicle System Dynamics 53 (May 2015), 1–23. doi:10.1080/00423114.2015.1039551

    2015

  47. [58]

    Morey, Jan-Willem Romeijn, and Jeffrey N

    Richard D. Morey, Jan-Willem Romeijn, and Jeffrey N. Rouder. 2016. The philos- ophy of Bayes factors and the quantification of statistical evidence. Journal of Mathematical Psychology 72 (June 2016), 6–18. doi:10.1016/j.jmp.2015.11.001

    2016 doi

  48. [59]

    Mirnig, Viktoria Sandor, Samuel Lesak, Arno Fallast, and Peter Fröhlich

    Lenart Motnikar, Marlen Jachek, Lukas Kröninger, Michael Gafert, Alexander G. Mirnig, Viktoria Sandor, Samuel Lesak, Arno Fallast, and Peter Fröhlich. 2023. Unravelling User Perspectives on Automated Urban Air Mobility: A Virtual Reality Approach. In Adjunct Proceedings of the...

    2023

  49. [60]

    Carl Jörgen Normark. 2015. Design and Evaluation of a Touch-Based Personalizable In-Vehicle User Interface. International Journal of Hu- man–Computer Interaction 31, 11 (2015), 731–745. doi:10.1080/10447318.2015. 1045240 arXiv:https://doi.org/10.1080/10447318.2015.1045240

    2015

  50. [61]

    Henry Pak, Lukas Asmer, Petra Kokus, Bianca I. Schuchardt, Albert End, Frank Meller, Karolin Schweiger, Christoph Torens, Carolina Barzantny, Dennis Becker, Johannes Maria Ernst, Florian Jäger, Tim Laudien, Nabih Naeem, Anne Papenfuß, Jan Pertz, Prajwal Shiva Prakasha, Patrick...

    2024

  51. [62]

    Kim Parker. 2023. About a third of U.S. workers who can work from home now do so all the time . Retrieved Nov 2, 2023 from https://www.pewresearch.org/short-reads/2023/03/30/about-a-third-of- us-workers-who-can-work-from-home-do-so-all-the-time/

    2023

  52. [63]

    Bob Pishue. 2021. 2021 INRIX global traffic scorecard

    2021

  53. [64]

    Bob Pishue. 2023. 2023 INRIX global traffic scorecard

    2023

  54. [65]

    Pukhova, C

    A. Pukhova, C. Llorca, A. Moreno, C. Staves, Q. Zhang, and R. Moeckel. 2021. Flying taxis revived: Can Urban air mobility reduce road congestion? Journal of Urban Mobility 1 (2021), 100002. doi:10.1016/j.urbmob.2021.100002

    2021

  55. [66]

    Suchithra Rajendran and Jake Shulman. 2020. Study of emerging air taxi network operation using discrete-event systems simulation approach. Journal of Air Transport Management 87 (2020), 101857. doi:10.1016/j.jairtraman.2020.101857

    2020

  56. [67]

    Suchithra Rajendran and Joshua Zack. 2019. Insights on strategic air taxi net- work infrastructure locations using an iterative constrained clustering approach. Transportation Research Part E: Logistics and Transportation Review 128 (2019), 470–505. doi:10.1016/j.tre.2019.06.003

    2019 doi

  57. [68]

    Michael Rietzler, Teresa Hirzle, Jan Gugenheimer, Julian Frommel, Thomas Dreja, and Enrico Rukzio. 2018. VRSpinning: Exploring the Design Space of a 1D Rotation Platform to Increase the Perception of Self-Motion in VR. InProceedings of the 2018 Designing Interactive Systems Co...

    2018

  58. [69]

    Ava Elizabeth Scott, Leon Reicherts, and Evropi Stefanidi. 2024. Doing CHI together: The benefits of a writing retreat for early-career HCI researchers. Interactions 31, 3 (2024), 8–9

    2024

  59. [70]

    Adams, and Nando De Fre- itas

    Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando De Fre- itas. 2016. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104, 1 (Jan. 2016), 148–175. doi:10.1109/JPROC.2015.2494218 Evaluating the Impact of Motion Fidelity on Optim...

    2016

  60. [71]

    YAW Motion Simulator. 2023. Virtual Reality Motion Simulator - Yaw VR . YAW Motion Simulator. Retrieved Apr 19, 2023 from https://www.yawvr.com/

    2023

  61. [72]

    Mel Slater and Sylvia Wilbur. 1997. A Framework for Immersive Virtual En- vironments (FIVE): Speculations on the Role of Presence in Virtual Environ- ments. Presence: Teleoperators and Virtual Environments 6, 6 (Dec. 1997), 603–616. doi:10.1162/pres.1997.6.6.603

    1997 doi

  62. [73]

    I. M. Sobol. 1967. On the distribution of points in a cube and the approximate evaluation of integrals. U. S. S. R. Comput. Math. and Math. Phys. 7 (1967), 86–112

    1967

  63. [74]

    Xu Sun, Jingpeng Li, Pinyan Tang, Siyuan Zhou, Xiangjun Peng, Hao Nan Li, and Qingfeng Wang. 2020. Exploring Personalised Autonomous Vehicles to Influence User Trust. Cognitive Computation 12, 6 (Nov. 2020), 1170–1186. doi:10.1007/ s12559-020-09757-x

    2020

  64. [75]

    Unity Technologies. 2022. Unity. Unity Technologies. https://unity.com/

    2022

  65. [76]

    Lorenzo Valente, Filippo Gabriele Pratticó, Marco Nobile, and Fabrizio Lamberti

  66. [77]

    Jinke D Van Der Laan, Adriaan Heino, and Dick De Waard. 1997. A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research Part C: Emerging Technologies 5, 1 (1997), 1–10

    1997

  67. [78]

    Volocopter GmbH. 2021. The roadmap to scalable urban air mobility . Volocopter GmbH. Retrieved June 3, 2023 from https://static1.squarespace.com/static/ 5d27bb3e330ac30001dc14fd/t/6165c265c89c61365aa517f5/1634058869091/ Volocopter+v2+Roadmap+to+Scalable+UAM.pdf

    2021

  68. [79]

    Annette Werner. 2018. New Colours for Autonomous Driving: An Evaluation of Chromaticities for the External Lighting Equipment of Autonomous Vehicles. doi:10.25538/tct.v0i1.692 Number: 1

    2018 doi

  69. [80]

    Matt N Williams, Rasmus A Bååth, and Michael C Philipp. 2017. Using Bayes Factors to Test Hypotheses in Developmental Research. Research in Human Development 14, 4 (Oct. 2017), 321–337. doi:10.1080/15427609.2017.1370964

    2017

  70. [81]

    Wobbrock and Julie A

    Jacob O. Wobbrock and Julie A. Kientz. 2016. Research Contributions in Human- Computer Interaction. Interactions 23, 3 (apr 2016), 38–44. doi:10.1145/2907069

    2016 doi

  71. [82]

    Marcel Woide, Mark Colley, Nicole Damm, and Martin Baumann. 2022. Effect of System Capability Verification on Conflict, Trust, and Behavior in Automated Vehicles. In Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applic...

    2022

  72. [83]

    Dohyeon Yeo, Gwangbin Kim, and Seungjun Kim. 2020. Toward Immersive Self-Driving Simulations: Reports from a User Study across Six Platforms. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . ACM, Honolulu HI USA, 1–12. doi:10.1145/3313831.3376787

    2020

  73. [84]

    Yijun Zhou, Yuki Koyama, Masataka Goto, and Takeo Igarashi. 2021. Interactive Exploration-Exploitation Balancing for Generative Melody Composition. In Pro- ceedings of the 26th International Conference on Intelligent User Interfaces (College Station, TX, USA) (IUI ’21). Associ...

    2021

  74. [1993]

    The International Journal of A viation Psychology 3, 3 (July 1993), 203–220

    Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness. The International Journal of A viation Psychology 3, 3 (July 1993), 203–220. doi:10.1207/s15327108ijap0303_3

    1993 doi

  75. [1994]

    Advances in robot kinematics and computational geometry (1994), 359–368

    Design and multi-objective optimization of a linkage for a haptic interface. Advances in robot kinematics and computational geometry (1994), 359–368

    1994

  76. [2016]

    Automated Driving

    Elaborating Feedback Strategies for Maintaining Automation in Highly CHI ’25, April 26-May 1, 2025, Yokohama, Japan Meinhardt et al. Automated Driving. In Proceedings of the 8th International Conference on Automo- tive User Interfaces and Interactive Vehicular Applications . A...

    2025

  77. [2021]

    Transportation Research Part C: Emerging Technologies 132 (2021), 103392

    Are commuter air taxis coming to your city? A ranking of 40 cities in the United States. Transportation Research Part C: Emerging Technologies 132 (2021), 103392. doi:10.1016/j.trc.2021.103392

    2021

  78. [2024]

    In 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)

    Towards Adaptive AR Interfaces for Passengers of Autonomous Urban Air Mobility Vehicles: Analyzing the Impact of Flight Phases and Visibility Conditions on User Experience Through Simulation. In 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Works...

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.