research-article

Exploring proprioceptive take-over requests for highly automated vehicles

Authors:

Sarah Faltaous,

Chris Schönherr,

Stefan SchneegassAuthors Info & Claims

MUM '19: Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia

Article No.: 13, Pages 1 - 6

https://doi.org/10.1145/3365610.3365644

Published: 26 November 2019 Publication History

Abstract

The uprising levels of autonomous vehicles allow the drivers to shift their attention to non-driving tasks while driving (i.e., texting, reading, or watching movies). However, these systems are prone to failure and, thus, depending on human intervention becomes crucial in critical situations. In this work, we propose using human actuation as a new mean of communicating take-over requests (TOR) through proprioception. We conducted a user study via a driving simulation in the presence of a complex working memory span task. We communicated TORs through four different modalities, namely, vibrotactile, audio, visual, and proprioception. Our results show that the vibrotactile condition yielded the fastest reaction time followed by proprioception. Additionally, proprioceptive cues resulted in the second best performance of the non-driving task following auditory cues.

References

[1]

Pavlo Bazilinskyy, Sebastiaan M Petermeijer, Veronika Petrovych, Dimitra Dodou, and Joost CF de Winter. 2018. Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays. Transportation research part F: traffic psychology and behaviour 56 (2018), 82--98.

[2]

Shadan Sadeghian Borojeni, Lewis Chuang, Wilko Heuten, and Susanne Boll. 2016. Assisting drivers with ambient take-over requests in highly automated driving. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 237--244.

Digital Library

[3]

Sarah Faltaous, Martin Baumann, Stefan Schneegass, and Lewis L. Chuang. 2018. Design Guidelines for Reliability Communication in Autonomous Vehicles. In Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '18). ACM, New York, NY, USA, 258--267.

Digital Library

[4]

Sarah Faltaous, Tonja Machulla, Martin Baumann, and Lewis Chuang. 2017. Developing a Highly Automated Driving Scenario to Investigate User Intervention: When Things Go Wrong. In Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct. ACM, 67--71.

Digital Library

[5]

Tom M Gasser and Daniel Westhoff. 2012. BASt-study: Definitions of automation and legal issues in Germany. In Proceedings of the 2012 road vehicle automation workshop. Automation Workshop.

[6]

S Gilman. 2002. Joint position sense and vibration sense: anatomical organisation and assessment. Journal of Neurology, Neurosurgery & Psychiatry 73, 5 (2002), 473--477. arXiv:https://jnnp.bmj.com/content/73/5/473.full.pdf

[7]

Christian Gold, Ilirjan Berisha, and Klaus Bengler. 2015. Utilization of drivetime-performing non-driving related tasks while driving highly automated. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 59. SAGE Publications Sage CA: Los Angeles, CA, 1666--1670.

[8]

Christian Gold, Daniel Damböck, Klaus Bengler, and Lutz Lorenz. 2013. Partially automated driving as a fallback level of high automation. In 6. Tagung Fahrerassistenzsysteme.

[9]

Christian Gold, Daniel Damböck, Lutz Lorenz, and Klaus Bengler. 2013. "Take over!" How long does it take to get the driver back into the loop?. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 57. SAGE Publications Sage CA: Los Angeles, CA, 1938--1942.

[10]

Christian Gold, Moritz Körber, David Lechner, and Klaus Bengler. 2016. Taking over control from highly automated vehicles in complex traffic situations: the role of traffic density. Human factors 58, 4 (2016), 642--652.

[11]

Pedro Lopes, Patrik Jonell, and Patrick Baudisch. 2015. Affordance++: allowing objects to communicate dynamic use. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 2515--2524.

Digital Library

[12]

Pedro Lopes, Doăa Yüksel, François Guimbretière, and Patrick Baudisch. 2016. Muscle-plotter: An interactive system based on electrical muscle stimulation that produces spatial output. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology. ACM, 207--217.

Digital Library

[13]

Huan Luo, Zuxiang Liu, and David Poeppel. 2010. Auditory cortex tracks both auditory and visual stimulus dynamics using low-frequency neuronal phase modulation. PLoS biology 8, 8 (2010), e1000445.

[14]

Annie WY Ng and Alan HS Chan. 2012. Finger response times to visual, auditory and tactile modality stimuli. In Proceedings of the international multiconference of engineers and computer scientists, Vol. 2. 1449--1454.

[15]

Sebastiaan Petermeijer, Pavlo Bazilinskyy, Klaus Bengler, and Joost De Winter. 2017. Take-over again: Investigating multimodal and directional TORs to get the driver back into the loop. Applied Ergonomics 62 (2017), 204--215.

[16]

Max Pfeiffer, Tim Duente, and Michael Rohs. 2016. Let your body move: a prototyping toolkit for wearable force feedback with electrical muscle stimulation. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 418--427.

Digital Library

[17]

Max Pfeiffer, Tim Dünte, Stefan Schneegass, Florian Alt, and Michael Rohs. 2015. Cruise Control for Pedestrians: Controlling Walking Direction Using Electrical Muscle Stimulation. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). ACM, New York, NY, USA, 2505--2514.

Digital Library

[18]

Bastian Pfleging, Maurice Rang, and Nora Broy. 2016. Investigating User Needs for Non-driving-related Activities During Automated Driving. In Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia (MUM '16). ACM, New York, NY, USA, 91--99.

Digital Library

[19]

Jonas Radlmayr, Christian Gold, Lutz Lorenz, Mehdi Farid, and Klaus Bengler. 2014. How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. In Proceedings of the human factors and ergonomics society annual meeting, Vol. 58. Sage Publications Sage CA: Los Angeles, CA, 2063--2067.

[20]

Shadan Sadeghian Borojeni, Susanne CJ Boll, Wilko Heuten, Heinrich H Bülthoff, and Lewis Chuang. 2018. Feel the movement: Real motion influences responses to take-over requests in highly automated vehicles. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 246.

Digital Library

[21]

Richard A Schmidt and Douglas E Young. 1987. Transfer of movement control in motor skill learning. In Transfer of learning. Elsevier, 47--79.

[22]

Stefan Schneegass and Rufat Rzayev. 2016. Embodied Notifications: Implicit Notifications Through Electrical Muscle Stimulation. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI '16). ACM, New York, NY, USA, 954--959.

Digital Library

[23]

JJ Scott and Robert Gray. 2008. A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving. Human factors 50, 2 (2008), 264--275.

[24]

Marcel Walch, Kristin Lange, Martin Baumann, and Michael Weber. 2015. Autonomous driving: investigating the feasibility of car-driver handover assistance. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 11--18.

Digital Library

[25]

Christopher D Wickens. 2004. Multiple resource time sharing models. In Handbook of human factors and ergonomics methods. CRC Press, 427--434.

[26]

M Zhou, DB Jones, SD Schwaitzberg, and CGL Cao. 2007. Role of haptic feedback and cognitive load in surgical skill acquisition. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 51. SAGE Publications Sage CA: Los Angeles, CA, 631--635.

Cited By

Jin LLiu XGuo BHan ZWang YCao YYang XShi J(2025)Impact of non-driving related task types, request modalities, and automation on driver takeover: A meta-analysisSafety Science10.1016/j.ssci.2024.106704181(106704)Online publication date: Jan-2025
https://doi.org/10.1016/j.ssci.2024.106704
Faltaous SKoelle MSchneegass S(2022)From Perception to Action: A Review and Taxonomy on Electrical Muscle Stimulation in HCIProceedings of the 21st International Conference on Mobile and Ubiquitous Multimedia10.1145/3568444.3568460(159-171)Online publication date: 27-Nov-2022
https://dl.acm.org/doi/10.1145/3568444.3568460
Faltaous SVogel JGiesler MSchneegass S(2022)Lessons Learnt from Using Electrical Muscle Stimulation to Actuate Agonist and Antagonist Muscle PairsProceedings of Mensch und Computer 202210.1145/3543758.3547570(528-532)Online publication date: 4-Sep-2022
https://dl.acm.org/doi/10.1145/3543758.3547570
Show More Cited By

Index Terms

Exploring proprioceptive take-over requests for highly automated vehicles
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction techniques

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cover image ACM Other conferences

MUM '19: Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia

November 2019

462 pages

ISBN:9781450376242

DOI:10.1145/3365610

General Chair:
Fabio Paternò
CNR-ISTI, Italy
,
Program Chairs:
Giulio Jacucci
University of Helsinki, Finland
,
Michael Rohs
University of Hannover, Germany
,
Carmen Santoro
CNR-ISTI, Italy

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 26 November 2019

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MUM 2019

MUM 2019: 18th International Conference on Mobile and Ubiquitous Multimedia

November 26 - 29, 2019

Pisa, Italy

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Overall Acceptance Rate 190 of 465 submissions, 41%

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Cited By

Jin LLiu XGuo BHan ZWang YCao YYang XShi J(2025)Impact of non-driving related task types, request modalities, and automation on driver takeover: A meta-analysisSafety Science10.1016/j.ssci.2024.106704181(106704)Online publication date: Jan-2025
https://doi.org/10.1016/j.ssci.2024.106704
Faltaous SKoelle MSchneegass S(2022)From Perception to Action: A Review and Taxonomy on Electrical Muscle Stimulation in HCIProceedings of the 21st International Conference on Mobile and Ubiquitous Multimedia10.1145/3568444.3568460(159-171)Online publication date: 27-Nov-2022
https://dl.acm.org/doi/10.1145/3568444.3568460
Faltaous SVogel JGiesler MSchneegass S(2022)Lessons Learnt from Using Electrical Muscle Stimulation to Actuate Agonist and Antagonist Muscle PairsProceedings of Mensch und Computer 202210.1145/3543758.3547570(528-532)Online publication date: 4-Sep-2022
https://dl.acm.org/doi/10.1145/3543758.3547570
Faltaous SHubert AKarolus JVilla SKosch TWozniak P(2022)EMStriker: Potentials of Enhancing the Training Process of Racket-based Sports via Electrical Muscle StimulationProceedings of the Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction10.1145/3490149.3505578(1-6)Online publication date: 13-Feb-2022
https://dl.acm.org/doi/10.1145/3490149.3505578
Liu WLi QWang ZWang WZeng CCheng B(2022)A Literature Review on Additional Semantic Information Conveyed from Driving Automation Systems to Drivers through Advanced In-Vehicle HMI Just Before, During, and Right After Takeover RequestInternational Journal of Human–Computer Interaction10.1080/10447318.2022.207466939:10(1995-2015)Online publication date: 26-May-2022
https://doi.org/10.1080/10447318.2022.2074669
Faltaous SGruenefeld USchneegass S(2021)Towards a Universal Human-Computer Interaction Model for Multimodal InteractionsProceedings of Mensch und Computer 202110.1145/3473856.3474008(59-63)Online publication date: 5-Sep-2021
https://dl.acm.org/doi/10.1145/3473856.3474008
Detjen HFaltaous SPfleging BGeisler SSchneegass S(2021)How to Increase Automated Vehicles’ Acceptance through In-Vehicle Interaction Design: A ReviewInternational Journal of Human–Computer Interaction10.1080/10447318.2020.186051737:4(308-330)Online publication date: 1-Jan-2021
https://doi.org/10.1080/10447318.2020.1860517
Faltaous SSchneegass S(2020)HCI Model: a Proposed Extension to Human-actuation TechnologiesProceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia10.1145/3428361.3432081(306-308)Online publication date: 22-Nov-2020
https://dl.acm.org/doi/10.1145/3428361.3432081

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