We're in This Together: Exploring Explanation Needs and Methods in Shared Automated Shuttle Buses

Manger, Carina; Preiwisch, Anna; Gambirasio, Chiara; Golks, Simon; Schuß, Martina; Riener, Andreas

doi:10.1145/3626705.3627798

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2024

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Cited by 3 publications

References 15 publications

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Robots in autonomous buses: Who hosts when no human is there?

Axelsson,

Vaddadi,

Bogdan

et al. 2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Robots in autonomous buses: Who hosts when no human is there?

Axelsson,

Vaddadi,

Bogdan

et al. 2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

“The bus is nothing without us”: Making Visible the Labor of Bus Operators amid the Ongoing Push Towards Transit Automation

Akridge,

Fan,

Tang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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TimelyTale: A Multimodal Dataset Approach to Assessing Passengers' Explanation Demands in Highly Automated Vehicles

Kim,

Hwang,

Seong

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Explanations in automated vehicles enhance passengers' understanding of vehicle decision-making, mitigating negative experiences by increasing their sense of control. These explanations help maintain situation awareness, even when passengers are not actively driving, and calibrate trust to match vehicle capabilities, enabling safe engagement in non-driving related tasks. While design studies emphasize timing as a crucial factor affecting trust, machine learning practices for explanation generation primarily focus on content rather than delivery timing. This discrepancy could lead to mistimed explanations, causing misunderstandings or unnecessary interruptions. This gap is partly due to alack of datasets capturing passengers' real-world demands and experiences with in-vehicle explanations. We introduce TimelyTale, an approach that records passengers' demands for explanations in automated vehicles. The dataset includes environmental, driving-related, and passenger-specific sensor data for context-aware explanations. Our machine learning analysis identifies proprioceptive and physiological data as key features for predicting passengers' explanation demands, suggesting their potential for generating timely, context-aware explanations. The TimelyTale dataset is available at https://doi.org/10.7910/DVN/CQ8UB0.

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We're in This Together: Exploring Explanation Needs and Methods in Shared Automated Shuttle Buses

Cited by 3 publications

References 15 publications

Robots in autonomous buses: Who hosts when no human is there?

Robots in autonomous buses: Who hosts when no human is there?

“The bus is nothing without us”: Making Visible the Labor of Bus Operators amid the Ongoing Push Towards Transit Automation

TimelyTale: A Multimodal Dataset Approach to Assessing Passengers' Explanation Demands in Highly Automated Vehicles

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