An Investigation of Drivers’ Dynamic Situational Trust in Conditionally Automated Driving

Ayoub, Jackie; Avetisyan, Lilit; Makki, Mustapha; Zhou, Feng

doi:10.1109/thms.2021.3131676

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…The State of California is suing Tesla for misleading the public about the Tesla autopilot capabilities, and news reporters have flooded social media with first-person views of Tesla performing dangerously on city roads [CNN (Director), 2021 ]. This has raised concerns about the risk of riding in a partially automated or fully autonomous vehicle and whether drivers or riders of vehicles can trust such vehicles (Endsley, 2017 ; Molnar et al, 2018 ; Ekman et al, 2019 ; Kraus et al, 2020 , 2021 ; Ayoub et al, 2022 ). Addressing such concerns will be paramount to public acceptance of AVs and to ensure its safe and successful integration into our traffic systems (Banks et al, 2018a ).…”

Section: Introductionmentioning

confidence: 99%

“…Based on the article by Lee and See ( 2004 ), trust is defined as the confidence an individual has in another agent within a context that involves elements of risk and vulnerability. A recent study has delved into trust and performance in AVs in which the driving automation represents the trusted agent (Molnar et al, 2018 ; Ekman et al, 2019 ; Petersen et al, 2019 ; Lee et al, 2021 ; Ayoub et al, 2022 ). Banks et al ( 2018b ) found that drivers interacted with AVs in a way that indicated over-trust and complacency in the AV.…”

Section: Introductionmentioning

confidence: 99%

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Group trust dynamics during a risky driving experience in a Tesla Model X

et al. 2023

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The growing concern about the risk and safety of autonomous vehicles (AVs) has made it vital to understand driver trust and behavior when operating AVs. While research has uncovered human factors and design issues based on individual driver performance, there remains a lack of insight into how trust in automation evolves in groups of people who face risk and uncertainty while traveling in AVs. To this end, we conducted a naturalistic experiment with groups of participants who were encouraged to engage in conversation while riding a Tesla Model X on campus roads. Our methodology was uniquely suited to uncover these issues through naturalistic interaction by groups in the face of a risky driving context. Conversations were analyzed, revealing several themes pertaining to trust in automation: (1) collective risk perception, (2) experimenting with automation, (3) group sense-making, (4) human-automation interaction issues, and (5) benefits of automation. Our findings highlight the untested and experimental nature of AVs and confirm serious concerns about the safety and readiness of this technology for on-road use. The process of determining appropriate trust and reliance in AVs will therefore be essential for drivers and passengers to ensure the safe use of this experimental and continuously changing technology. Revealing insights into social group–vehicle interaction, our results speak to the potential dangers and ethical challenges with AVs as well as provide theoretical insights on group trust processes with advanced technology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Group trust dynamics during a risky driving experience in a Tesla Model X

et al. 2023

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“…However, the benefits of ADAS will not be reached if they are not used properly or drivers do not have proper trust in them due to possible ADAS failures [4,6]. Reagan and McCartt [49] found that under-trust and false alarms might be the main causes that led to the disuse of ADAS.…”

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confidence: 99%

Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data

Ayoub

Wang

et al. 2022

Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations of the current ADAS and their possible solutions. This study 1) investigated the limitations and solutions of ADAS through a literature review, 2) identified the causes and effects of ADAS through consumer complaints using natural language processing models, and 3) compared the major differences between the two. These two lines of research identified similar categories of ADAS causes, including human factors, environmental factors, and vehicle factors. However, academic research focused more on human factors of ADAS issues and proposed advanced algorithms to mitigate such issues while drivers complained more of vehicle factors of ADAS failures, which led to associated top consequences. The findings from these two sources tend to complement each other and provide important implications for the improvement of ADAS in the future.

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