Impact of Monitoring Requests on Trust,Acceptance,Blame,and Praise of Autonomous Vehicles

Abstract

Vehicles with autonomous features are more prevalent in today’s society, though as the level of autonomation increases, so does the vehicle system’s control of the vehicle. As the driving control shifts

from human to the vehicle system, concerns arise regarding the attribution of responsibility and blame following critical events (e.g., collisions or near-misses). In this work-in-progress study, we aim to understand how the public attributes blame and praise to both humans and autonomous vehicles (AVs) following critical events. In addition, we examine how an AI driving assistant that administers Monitoring Requests influences blame and praise attributions. Furthermore, we examine differences in acceptance, trust, and perceived anthropomorphism between an AV with and without an AI driving assistant. Preliminary results are provided followed by a discussion of the expected results and potential impact for research and legal issues.

Get full access to this article

View all access options for this article.

References

Awad

Levine

Kleiman-Weiner

Dsouza

Tenenbaum

J. B.

Shariff

Bonnefon

J.-F.

Rahwan

(2020). Drivers are blamed more than their automated cars when both make mistakes. Nature Human Behaviour, 4(2), 134–143. https://doi.org/10.1038/s41562-0190762-8

Bennett

J. M.

Challinor

K. L.

Modesto

Prabhakharan

(2020). Attribution of blame of crash causation across varying levels of vehicle automation. Safety Science, 132, 104968. https://doi.org/10.1016/j.ssci.2020.104968

Burke

(2022, January 21). Tesla driver charged with manslaughter in deadly Autopilot crash raises new legal questions about automated driving tech. NBC News. https://www.nbcnews.com/news/usnews/tesla-driver-charged-manslaughter-deadly-autopilot-crash-raisesnew-le-rcna12987

Conger

(2020). Driver charged in Uber’s fatal 2018 autonomous car crash. Retrieved from https://www.nytimes.com/2020/09/15/technology/uberautonomous-crash-drivercharged.html

Copp

C. J.

Cabell

J. J.

Kemmelmeier

(2021). Plenty of blame to go around: Attributions of responsibility in a fatal autonomous vehicle accident. Current Psychology. https://doi.org/10.1007/s12144-02101956-5

Dong

Lawson

Olsen

Jeon

(2020). Female Voice Agents in Fully Autonomous Vehicles Are Not Only More Likeable and Comfortable, But Also More Competent. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64(1), 1033–1037. https://doi.org/10.1177/1071181320641248

Holthausen

B. E.

Wintersberger

Walker

B. N.

Riener

(2020). Situational Trust Scale for Automated Driving (STS-AD): Development and Initial Validation. 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 40–47. https://doi.org/10.1145/3409120.3410637

Hong

J. W.

(2020). Why Is Artificial Intelligence Blamed More? Analysis of Faulting Artificial Intelligence for Self-Driving Car Accidents in Experimental Settings. International Journal of Human–Computer Interaction, 36(18), 1768–1774. https://doi.org/10.1080/10447318.2020.1785693

Koo

Shin

Steinert

Leifer

(2016). Understanding driver responses to voice alerts of autonomous car operations. International Journal of Vehicle Design, 70(4), 377.https://doi.org/10.1504/IJVD.2016.076740

10.

Large

D. R.

Burnett

Anyasodo

Skrypchuk

(2016). Assessing Cognitive Demand during Natural Language Interactions with a Digital Driving Assistant. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 67–74. https://doi.org/10.1145/3003715.3005408

11.

Lee

Ratan

Park

(2019). The Voice Makes the Car: Enhancing Autonomous Vehicle Perceptions and Adoption Intention through Voice Agent Gender and Style. Multimodal Technologies and Interaction, 3(1), 20. https://doi.org/10.3390/mti3010020

12.

Liu

(2021). Blame Attribution Asymmetry in Human– Automation Cooperation. Risk Analysis, risa.13674. https://doi.org/10.1111/risa.13674

13.

Zhang

Feldhütter

Happee

Martens

De Winter

J. C.F

. (2019). Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance.Transportation Research Part F: Traffic Psychology and Behaviour, 63, 22–37. https://doi.org/10.1016/j.trf.2019.03.018

14.

Lugano

(2017). Virtual assistants and self-driving cars. 2017 15th International Conference on ITS Telecommunications (ITST), 1–5. https://doi.org/10.1109/ITST.2017.7972192

15.

National Transportation Safety Board. (2019). Collision between vehicle controlled by developmental automated driving system and pedestrian Tempe, Arizona March 18, 2018. Highway Accident Report. Report no. NTSB/HAR-19/03. Washington, DC: National Transportation Safety Board

16.

Naujoks

Kiesel

Neukum

(2016). Cooperative warning systems: The impact of false and unnecessary alarms on drivers’ compliance. Accident Analysis & Prevention, 97, 162–175. https://doi.org/10.1016/j.aap.2016.09.009

17.

Petermeijer

Bazilinskyy

Bengler

de Winter

(2017). Takeover again: Investigating multimodal and directional TORs to get the driver back into the loop. Applied Ergonomics, 62, 204–215. https://doi.org/10.1016/j.apergo.2017.02.023

18.

Pöllänen

Read

G. J. M.

Lane

B. R.

Thompson

Salmon

P. M.

(2020). Who is to blame for crashes involving autonomous vehicles? Exploring blame attribution across the road transport system. Ergonomics, 63(5), 525–537. https://doi.org/10.1080/00140139.2020.1744064

19.

SAE International (2016). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicle (J3016_201609).

20.

Van Der Laan

J. D.

Heino

De Waard

. (1997). A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research Part C: Emerging Technologies, 5(1), 1–10. https://doi.org/10.1016/S0968-090X(96)00025-3

21.

Vlakveld

van Nes

de Bruin

Vissers

van der Kroft

(2018). Situation awareness increases when drivers have more time to take over the wheel in a Level 3 automated car: A simulator study.Transportation Research Part F: Traffic Psychology and Behaviour, 58,917–929. https://doi.org/10.1016/j.trf.2018.07.025

22.

Walch

Sieber

Hock

Baumann

Weber

. (2016). Towards Cooperative Driving: Involving the Driver in an Autonomous Vehicle’s Decision Making. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 261–268. https://doi.org/10.1145/3003715.3005458

23.

Waytz

Heafner

Epley

(2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52, 113–117. https://doi.org/10.1016/j.jesp.2014.01.005

24.

Wong

P. N. Y.

Brumby

D. P.

Babu

H. V. R.

Kobayashi

(2019). Voices in Self-Driving Cars Should be Assertive to More Quickly Grab a Distracted Driver’s Attention. Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 165–176. https://doi.org/10.1145/3342197.3344535

25.

Yang

J.H.

Lee

S.C.

Nadri

Kim

Shin

Jeon

(2022) Multimodal displays for takeover requests. In: Riener

Jeon

Alvarez

. (Eds.), User Experience Design in the Era of Automated Driving (pp. 397-424). Springer. https://doi-org/10.1007/978-3-03077726-5_15

26.

Zeeb

Buchner

Schrauf

(2016). Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving. Accident Analysis & Prevention, 92, 230–239. https://doi.org/10.1016/j.aap.2016.04.002