First Workshop on Trust in the Age of Automated Driving

Noah, Brittany E.; Wintersberger, Philipp; Mirnig, Alexander G.; Thakkar, Shailie; Yan, Fei; Gable, Thomas M.; Kraus, Johannes; McCall, Roderick

doi:10.1145/3131726.3131733

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…In the absence of a human driver (i.e., fully automated vehicle), pedestrians took their crossing decision mainly dependent on the distance of the approaching car, which is in line with the findings of prior investigations (Oxley et al, 2005;Yannis et al, 2013;Moore et al, 2019;Dey et al, 2020c;Holländer et al, 2020b). However, we believe that explicit communication has the potential to enhance trust of automated driving and should therefore not be neglected (Noah et al, 2017;Wintersberger et al, 2018;Holländer et al, 2019b). Projections scored second best for intuitive understanding and slightly exceeded the baseline in the point "Low subjective mental workload."…”

Section: Comprehensibilitysupporting

confidence: 88%

“…Previous studies have highlighted that safety (objective and subjective), comprehensibility, and usability are decisive factors when evaluating VRU-AV communication concepts (Noah et al, 2017;Wintersberger et al, 2018). To determine which concepts lead to the most optimal results, we aimed to measure these factors through an immersive virtual reality investigation that simulates a realistic first-person view experience.…”

Section: User Studymentioning

confidence: 99%

See 1 more Smart Citation

Take It to the Curb: Scalable Communication Between Autonomous Cars and Vulnerable Road Users Through Curbstone Displays

Holländer¹,

Hoggenmueller²,

Gruber³

et al. 2022

Front. Comput. Sci.

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Automated driving will require new approaches to the communication between vehicles and vulnerable road users (VRUs) such as pedestrians, e.g., through external human–machine interfaces (eHMIs). However, the majority of eHMI concepts are neither scalable (i.e., take into account complex traffic scenarios with multiple vehicles and VRUs), nor do they optimize traffic flow. Speculating on the upgrade of traffic infrastructure in the automated city, we propose Smart Curbs, a scalable communication concept integrated into the curbstone. Using a combination of immersive and non-immersive prototypes, we evaluated the suitability of our concept for complex urban environments in a user study (N = 18). Comparing the approach to a projection-based eHMI, our findings reveal that Smart Curbs are safer to use, as our participants spent less time on the road when crossing. Based on our findings, we discuss the potential of Smart Curbs to mitigate the scalability problem in AV-pedestrian communication and simultaneously enhance traffic flow.

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Section: Comprehensibilitysupporting

confidence: 88%

Section: User Studymentioning

confidence: 99%

Take It to the Curb: Scalable Communication Between Autonomous Cars and Vulnerable Road Users Through Curbstone Displays

Holländer¹,

Hoggenmueller²,

Gruber³

et al. 2022

Front. Comput. Sci.

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“…The industry and academics have been testing methods with the potential to improve levels of trust and acceptance of AVs (Noah et al, 2017). One of these methods is system transparency, when users are presented with information about the status of the system (Lee and See, 2004).…”

Section: Introductionmentioning

confidence: 99%

The influence of system transparency on trust: Evaluating interfaces in a highly automated vehicle

Oliveira

Burns

Luton

et al. 2020

Transportation Research Part F: Traffic Psychology and Behaviou

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“…Although there are many assumptions made, we still believe the finding of this study will contribute to better user-interface designs for supervised autonomous vehicles. We also expect that further investigation on other important factors [18] in autonomous driving, such as reliance behaviors of drivers with non-drivingrelated activities, will help to enhance our framework to be applicable to autonomous vehicle in general.…”

Section: Resultsmentioning

confidence: 99%

Adaptive Trust Calibration for Supervised Autonomous Vehicles

Okamura

Yamada

2018

Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Poor trust calibration in autonomous vehicles often degrades total system performance in safety or efficiency. Existing studies have primarily examined the importance of system transparency of autonomous systems to maintain proper trust calibration, with little emphasis on how to detect over-trust and under-trust nor how to recover from them. With the goal of addressing these research gaps, we first provide a framework to detect a calibration status on the basis of the user's behavior of reliance. We then propose a new concept with cognitive cues called trust calibration cues (TCCs) to trigger the user to quickly restore appropriate trust calibration. With our framework and TCCs, a novel method of adaptive trust calibration is explored in this study. We will evaluate our framework and examine the effectiveness of TCCs with a newly developed online drone simulator.

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First Workshop on Trust in the Age of Automated Driving

Cited by 15 publications

References 16 publications

Take It to the Curb: Scalable Communication Between Autonomous Cars and Vulnerable Road Users Through Curbstone Displays

Take It to the Curb: Scalable Communication Between Autonomous Cars and Vulnerable Road Users Through Curbstone Displays

The influence of system transparency on trust: Evaluating interfaces in a highly automated vehicle

Adaptive Trust Calibration for Supervised Autonomous Vehicles

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