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Assertive Takeover Requests: Immediate and Sustained Effects on Stress and Performance

Authors:

Lenart Motnikar,

Alexander Mirnig,

Peter FröhlichAuthors Info & Claims

AutomotiveUI '23: Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Pages 103 - 111

https://doi.org/10.1145/3580585.3607174

Published: 18 September 2023 Publication History

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Abstract

In conditionally automated driving, assertive takeover requests (TORs) have been found to elicit faster responses in the transition from automated to manual control. However, despite this apparent benefit, concerns have been raised that assertive signals can stress the drivers, resulting in worse takeover quality and subsequent driving performance.

To investigate the phenomenon, we conducted a driving simulator study exploring the effects of assertive and non-assertive TOR signals. In a series of tasks where the drivers were required to take over control of the vehicle to manually maneuver a critical situation, immediate and sustained measures of driving performance, stress, and subjective perceptions were compared.

Consistent with previous research, the assertive signal led to shorter reaction times and stronger braking. However, no other immediate or delayed differences in the measures were observed. These findings suggest that assertive TOR signals might be a preferable design option, however, the phenomenon should be further explored.

References

[1]

Shubham Agrawal and Srinivas Peeta. 2021. Evaluating the impacts of driver’s pre-warning cognitive state on takeover performance under conditional automation. Transportation Research Part F: Traffic Psychology and Behaviour 83 (Nov. 2021), 80–98. https://doi.org/10.1016/j.trf.2021.10.004

[2]

Shubham Agrawal and Srinivas Peeta. 2021. Evaluating the impacts of situational awareness and mental stress on takeover performance under conditional automation. Transportation Research Part F: Traffic Psychology and Behaviour 83 (Nov. 2021), 210–225. https://doi.org/10.1016/j.trf.2021.10.002

[3]

Mohamed Taher Alrefaie, Stever Summerskill, and Thomas W Jackon. 2019. In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles. Accident Analysis & Prevention 131 (Oct. 2019), 180–190. https://doi.org/10.1016/j.aap.2019.06.011

[4]

Norah H. Alsuraykh, Max L. Wilson, Paul Tennent, and Sarah Sharples. 2019. How Stress and Mental Workload are Connected. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, Trento Italy, 371–376. https://doi.org/10.1145/3329189.3329235

Digital Library

[5]

AVSimulation. 2023. AVSimulation | AVS : Automotive simulation software and solutions. https://www.avsimulation.com/

[6]

Francesco Biondi, Riccardo Rossi, Massimiliano Gastaldi, and Claudio Mulatti. 2014. Beeping ADAS: Reflexive effect on drivers’ behavior. Transportation Research Part F: Traffic Psychology and Behaviour 25 (July 2014), 27–33. https://doi.org/10.1016/j.trf.2014.04.020

[7]

Christian Braunagel, Wolfgang Rosenstiel, and Enkelejda Kasneci. 2017. Ready for Take-Over? A New Driver Assistance System for an Automated Classification of Driver Take-Over Readiness. IEEE Intelligent Transportation Systems Magazine 9, 4 (2017), 10–22. https://doi.org/10.1109/MITS.2017.2743165

[8]

Chelsea A. DeGuzman, Dina Kanaan, and Birsen Donmez. 2022. Attentive User Interfaces: Adaptive Interfaces that Monitor and Manage Driver Attention. In User Experience Design in the Era of Automated Driving, Andreas Riener, Myounghoon Jeon, and Ignacio Alvarez (Eds.). Vol. 980. Springer International Publishing, Cham, 305–334. https://doi.org/10.1007/978-3-030-77726-5_12

[9]

Christophe Deniaud, Vincent Honnet, Benoit Jeanne, and Daniel Mestre. 2015. The concept of “presence” as a measure of ecological validity in driving simulators. Journal of Interaction Science 3, 1 (July 2015), 1. https://doi.org/10.1186/s40166-015-0005-z

[10]

Na Du, X. Jessie Yang, and Feng Zhou. 2020. Psychophysiological responses to takeover requests in conditionally automated driving. Accident Analysis & Prevention 148 (Dec. 2020), 105804. https://doi.org/10.1016/j.aap.2020.105804

[11]

Johan Fagerlönn. 2010. Distracting Effects of Auditory Warnings on Experienced Drivers. In The 16th International Conference on Auditory Display (ICAD-2010). Georgia Institute of Technology, Washington D.C., USA, 127–132. http://hdl.handle.net/1853/49879

[12]

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?Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, 1 (Sept. 2013), 1938–1942. https://doi.org/10.1177/1541931213571433

[13]

Christian Gold, Riender Happee, and Klaus Bengler. 2018. Modeling take-over performance in level 3 conditionally automated vehicles. Accident Analysis & Prevention 116 (July 2018), 3–13. https://doi.org/10.1016/j.aap.2017.11.009

[14]

BMW Group. 2022. PressClub Global · Audios.https://www.press.bmwgroup.com/global/audio/search/category:sound/

[15]

Timotej Gruden, Sašo Tomažič, Jaka Sodnik, and Grega Jakus. 2022. A user study of directional tactile and auditory user interfaces for take-over requests in conditionally automated vehicles. Accident Analysis & Prevention 174 (Sept. 2022), 106766. https://doi.org/10.1016/j.aap.2022.106766

[16]

Kathrin Hilgarter, Karin Schmid-Zalaudek, Regina Csanády-Leitner, Manfred Mörtl, Andreas Rössler, and Helmut Karl Lackner. 2021. Phasic heart rate variability and the association with cognitive performance: A cross-sectional study in a healthy population setting. PLOS ONE 16, 3 (March 2021), e0246968. https://doi.org/10.1371/journal.pone.0246968

[17]

iMotions A/S. 2022. iMotions - Powering Human Insights. https://imotions.com/

[18]

SAE International. 2021. J3016_202104: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles - SAE International. https://www.sae.org/standards/content/j3016_202104/

[19]

Christian P. Janssen, Remo M. A. van der Heiden, Stella F. Donker, and J. Leon Kenemans. 2019. Measuring susceptibility to alerts while encountering mental workload. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings. ACM, Utrecht Netherlands, 415–420. https://doi.org/10.1145/3349263.3351524

Digital Library

[20]

Laora Kerautret, Stephanie Dabic, and Jordan Navarro. 2023. Exploration of driver stress when resuming control from highly automated driving in an emergency situation. Transportation Research Part F: Traffic Psychology and Behaviour 93 (Feb. 2023), 222–234. https://doi.org/10.1016/j.trf.2023.01.016

[21]

David R. Large, Gary Burnett, Davide Salanitri, Anneka Lawson, and Elizabeth Box. 2019. A Longitudinal Simulator Study to Explore Drivers’ Behaviour in Level 3 Automated Vehicles. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, Utrecht Netherlands, 222–232. https://doi.org/10.1145/3342197.3344519

Digital Library

[22]

Shu Ma, Wei Zhang, Zhen Yang, Chunyan Kang, Changxu Wu, Chunlei Chai, Jinlei Shi, Yilin Zeng, and Hongting Li. 2021. Take over Gradually in Conditional Automated Driving: The Effect of Two-stage Warning Systems on Situation Awareness, Driving Stress, Takeover Performance, and Acceptance. International Journal of Human–Computer Interaction 37, 4 (Feb. 2021), 352–362. https://doi.org/10.1080/10447318.2020.1860514

[23]

Bruce Mehler, Bryan Reimer, Joseph F. Coughlin, and Jeffery A. Dusek. 2009. Impact of Incremental Increases in Cognitive Workload on Physiological Arousal and Performance in Young Adult Drivers. Transportation Research Record: Journal of the Transportation Research Board 2138, 1 (Jan. 2009), 6–12. https://doi.org/10.3141/2138-02

[24]

Vadim Melnicuk, Simon Thompson, Paul Jennings, and Stewart Birrell. 2021. Effect of cognitive load on drivers’ State and task performance during automated driving: Introducing a novel method for determining stabilisation time following take-over of control. Accident Analysis & Prevention 151 (March 2021), 105967. https://doi.org/10.1016/j.aap.2020.105967

[25]

Natasha Merat, A. Hamish Jamson, Frank C. H. Lai, Michael Daly, and Oliver M. J. Carsten. 2014. Transition to manual: Driver behaviour when resuming control from a highly automated vehicle. Transportation Research Part F: Traffic Psychology and Behaviour 27 (Nov. 2014), 274–282. https://doi.org/10.1016/j.trf.2014.09.005

[26]

Natasha Merat, Bobbie Seppelt, Tyron Louw, Johan Engström, John D. Lee, Emma Johansson, Charles A. Green, Satoshi Katazaki, Chris Monk, Makoto Itoh, Daniel McGehee, Takashi Sunda, Kiyozumi Unoura, Trent Victor, Anna Schieben, and Andreas Keinath. 2019. The “Out-of-the-Loop” concept in automated driving: proposed definition, measures and implications. Cognition, Technology & Work 21, 1 (Feb. 2019), 87–98. https://doi.org/10.1007/s10111-018-0525-8

Digital Library

[27]

Mario Muñoz-Organero and Victor Corcoba-Magaña. 2017. Predicting Upcoming Values of Stress While Driving. IEEE Transactions on Intelligent Transportation Systems 18, 7 (July 2017), 1802–1811. https://doi.org/10.1109/TITS.2016.2618424

Digital Library

[28]

Erfan Pakdamanian, Nauder Namaky, Shili Sheng, Inki Kim, James Arthur Coan, and Lu Feng. 2020. Toward Minimum Startle After Take-Over Request: A Preliminary Study of Physiological Data. In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, Virtual Event DC USA, 27–29. https://doi.org/10.1145/3409251.3411715

Digital Library

[29]

Polar. 2023. Polar H10 | Polar USA. https://www.polar.com/us-en/sensors/h10-heart-rate-sensor

[30]

Ioannis Politis, Stephen Brewster, and Frank Pollick. 2015. To Beep or Not to Beep? Comparing Abstract versus Language-Based Multimodal Driver Displays. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems(CHI ’15). Association for Computing Machinery, New York, NY, USA, 3971–3980. https://doi.org/10.1145/2702123.2702167

Digital Library

[31]

Ioannis Politis, Stephen A. Brewster, and Frank Pollick. 2014. Evaluating multimodal driver displays under varying situational urgency. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’14). Association for Computing Machinery, New York, NY, USA, 4067–4076. https://doi.org/10.1145/2556288.2556988

Digital Library

[32]

Fabienne Roche and Stefan Brandenburg. 2018. Should the urgency of auditory-tactile takeover requests match the criticality of takeover situations?. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). Institute of Electrical and Electronics Engineers (IEEE), Maui, Hawaii, USA, 1035–1040. https://doi.org/10.1109/ITSC.2018.8569650

Digital Library

[33]

Robert Rosenthal. 1994. Parametric measures of effect size. In The handbook of research synthesis. Russell Sage Foundation, New York, NY, US, 231–244.

[34]

Siby Samuel, Avinoam Borowsky, Shlomo Zilberstein, and Donald L. Fisher. 2016. Minimum Time to Situation Awareness in Scenarios Involving Transfer of Control from an Automated Driving Suite. Transportation Research Record 2602, 1 (Jan. 2016), 115–120. https://doi.org/10.3141/2602-14

[35]

Harsh Sanghavi, Yiqi Zhang, and Myounghoon Jeon. 2020. Effects of Anger and Display Urgency on Takeover Performance in Semi-automated Vehicles. In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, Virtual Event DC USA, 48–56. https://doi.org/10.1145/3409120.3410664

Digital Library

[36]

Priscilla N. Y. Wong, Duncan P. Brumby, Harsha Vardhan Ramesh Babu, and Kota Kobayashi. 2019. "Watch Out!": Semi-Autonomous Vehicles Using Assertive Voices to Grab Distracted Drivers’ Attention. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems(CHI EA ’19). Association for Computing Machinery, New York, NY, USA, 1–6. https://doi.org/10.1145/3290607.3312838

Digital Library

[37]

Haoran Wu, Chaozhong Wu, Nengchao Lyu, and Jiannan Li. 2022. Does a faster takeover necessarily mean it is better? A study on the influence of urgency and takeover-request lead time on takeover performance and safety. Accident Analysis & Prevention 171 (June 2022), 106647. https://doi.org/10.1016/j.aap.2022.106647

[38]

Weiru Yin, Chen Chai, David S. Hurwitz, Siqi Wang, Jiaxin Guo, and Xiao Liu. 2022. Evaluating Effects of Urgent Takeover Requests on Driver Stress through Galvanic Skin Response Analysis. In CICTP 2022: Intelligent, Green, and Connected Transportation. American Society of Civil Engineers, Changsha Hunan Province, China, 1661–1671. https://doi.org/10.1061/9780784484265.156

[39]

Kathrin Zeeb, Axel Buchner, and Michael Schrauf. 2015. What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accident Analysis & Prevention 78 (May 2015), 212–221. https://doi.org/10.1016/j.aap.2015.02.023

Cited By

Gruden TTomažič SJakus G(2024)Post-Takeover Proficiency in Conditionally Automated Driving: Understanding Stabilization Time with Driving and Physiological SignalsSensors10.3390/s2410319324:10(3193)Online publication date: 17-May-2024
https://doi.org/10.3390/s24103193
Papagni GZafari SSchrammel JTscheligi M(2024)Automating Logistics Operations: Qualitative Insights from Four European SitesLogistics10.3390/logistics80401148:4(114)Online publication date: 8-Nov-2024
https://doi.org/10.3390/logistics8040114
Passero SPelikan HBroth MBrown B(2024)Honkable Gestalts: Why Autonomous Vehicles Get Honked AtProceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3640792.3675732(317-328)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640792.3675732
Show More Cited By

Index Terms

Assertive Takeover Requests: Immediate and Sustained Effects on Stress and Performance
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
  2. Interaction design
    1. Empirical studies in interaction design

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cover image ACM Conferences

AutomotiveUI '23: Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 2023

352 pages

ISBN:9798400701054

DOI:10.1145/3580585

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 18 September 2023

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AutomotiveUI '23

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AutomotiveUI '23: 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 18 - 22, 2023

Ingolstadt, Germany

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Cited By

Gruden TTomažič SJakus G(2024)Post-Takeover Proficiency in Conditionally Automated Driving: Understanding Stabilization Time with Driving and Physiological SignalsSensors10.3390/s2410319324:10(3193)Online publication date: 17-May-2024
https://doi.org/10.3390/s24103193
Papagni GZafari SSchrammel JTscheligi M(2024)Automating Logistics Operations: Qualitative Insights from Four European SitesLogistics10.3390/logistics80401148:4(114)Online publication date: 8-Nov-2024
https://doi.org/10.3390/logistics8040114
Passero SPelikan HBroth MBrown B(2024)Honkable Gestalts: Why Autonomous Vehicles Get Honked AtProceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3640792.3675732(317-328)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3640792.3675732
Gruden TJakus G(2024)Exploring Driver Diversity in Post-Takeover Stabilization Based on Demographic Data2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME62383.2024.10796037(1-6)Online publication date: 4-Nov-2024
https://doi.org/10.1109/ICECCME62383.2024.10796037

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