Providing Explainability in Safety-Critical Automated Driving Situations through Augmented Reality Windshield HMIs

Manger, Carina; Peintner, Jakob; Hoffmann, Marion; Probst, Mirella; Wennmacher, Raphael; Riener, Andreas

doi:10.1145/3581961.3609874

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2024

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Cited by 3 publications

References 24 publications

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I Don't Know What Happens, But… Do I Really Need that Explanation?

Wang,

Jeon

2024

Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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I Don't Know What Happens, But… Do I Really Need that Explanation?

Wang,

Jeon

2024

Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

View full text Add to dashboard Cite

Explaining Away Control: Exploring the Relationship between Explainable AI and Passengers' Desire for Control in Automated Vehicles

Peintner,

Manger,

Berger

et al. 2024

Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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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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Providing Explainability in Safety-Critical Automated Driving Situations through Augmented Reality Windshield HMIs

Cited by 3 publications

References 24 publications

I Don't Know What Happens, But… Do I Really Need that Explanation?

I Don't Know What Happens, But… Do I Really Need that Explanation?

Explaining Away Control: Exploring the Relationship between Explainable AI and Passengers' Desire for Control in Automated Vehicles

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

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