Don't Be Alarmed

Gang, Nick; Sibi, Srinath; Michon, Romain; Mok, Brian; Chafe, Chris; Ju, Wendy

doi:10.1145/3239060.3265636

Cited by 32 publications

(4 citation statements)

References 10 publications

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“…Future designs could incorporate larger visual cues and improved audio alerts that better alert the driver. As participants often felt distracted by jarring loud beeps, it is further important that audio alerts sound pleasing, which may be achieved through musical sonification (Gang et al, 2018 ; Seppelt and Lee, 2019 ; Chen and Chen, 2021 ; Nadri et al, 2023 ). Aside from multimodal cues, we recommend a different transfer of control maneuver (e.g., button) that is more straightforward than the current lever maneuver.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…We asked the participants to elaborate on what was happening in the video and explain their reasons for losing and regaining the trust. In this way, we allowed the participants to relive and reflect on their experience and map their thinking with the context [11]. After all five videos, they filled in a questionnaire for demographic data.…”

Section: Methods and Proceduresmentioning

confidence: 99%

An Online Study to Explore Trust in Highly Automated Vehicle in Non-Critical Automated Driving Scenarios

Dong

Martens

Pfleging

2021

13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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While using highly automated systems, various non-critical automated driving scenarios can be identified in which trust plays a role. In this study, we investigated the change of trust in these scenarios with a digital "Feeling of Trust" indicator, through video-based online experiments simulating automated driving. Initial results show that trust even changes in these scenarios and revealed multiple influential factors. While trust seems to drop consistently in certain cases, we found individual differences in other events. With our experimental setup and findings, we provide a tool to examine trust aspects in an online study. This contributes to the understanding of how to design human-vehicle interactions in highly automated cars with the goal to calibrate trust under ordinary non-critical events. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); HCI design and evaluation methods.

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“…Providing an intuitive human-computer interaction interface is critical to satisfying users, which includes drivers and passengers. A range of studies has leveraged humancentered XAI design that employs visual, audio, and textual modalities to communicate a vehicle's real-time decisions to its passengers [17]. Incorporating such feedback in vehicle design can significantly improve user experience, thus establishing the need for intelligent AI systems for the broader acceptance of autonomous vehicles.…”

Section: Explainable Ai-based Driver Monitoring Systemmentioning

confidence: 99%

An Explainable Attention Zone Estimation for Level 3 Autonomous Driving

Yahyaabadi,

Nikan

2023

IEEE Access

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Accurately assessing the driver's situational awareness is crucial in level 3 (L 3 ) autonomous driving, where the driver is in the loop. Estimating the attention zone provides essential information about the drivers' on/off-road visual attention and determines their readiness to take over the control from the autonomous agent in complicated situations. This paper proposes a double-phase pipeline to improve the explainability and accuracy of the attention zone estimation using an intermediate gaze regression layer, where the true relationships between the input images and output zone labels are interpretable. The proposed GazeMobileNet, a lightweight deep neural network, in the first phase, achieved state-of-the-art performance in estimating the gaze vector in the MPIIGaze dataset, with MAE of 2.37 degrees. The model was used to extract the corresponding gaze vectors from the LISA V2, which is a driving dataset with the incabin attention zone labels. As LISA V2 does not contain gaze vector labels, an unsupervised clustering approach was proposed in the second phase to categorize the driver's gaze vectors and map them to the corresponding attention zones. The proposed method demonstrated improved accuracy and robustness in the zone classification task. This model achieved the accuracies of 75.67% and 83.08% for attention zone estimation under "daytime without eyeglasses" and "nighttime without eyeglasses" capture conditions, respectively. Furthermore, the proposed model surpassed the recent research on that dataset by 73.11% and 74.02% accuracies under the "daytime with eyeglasses" and "nighttime with eyeglasses" capture conditions, respectively.INDEX TERMS Level 3 autonomy, gaze estimation, GazeMobileNet, driver's attention zone, explainable clustering.

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Don't Be Alarmed

Cited by 32 publications

References 10 publications

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

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

An Online Study to Explore Trust in Highly Automated Vehicle in Non-Critical Automated Driving Scenarios

An Explainable Attention Zone Estimation for Level 3 Autonomous Driving

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