TimelyTale: A Multimodal Dataset Approach to Assessing Passengers' Explanation Demands in Highly Automated Vehicles
Name
3678544.pdf
Size
70.03 MB
Format
Adobe PDF
Checksum (MD5)
0caeaef8ac603ed966644940a0171129
Author(s)
Kim, Gwangbin
Hwang, Seokhyun
Seong, Minwoo
Yeo, Dohyeon
Rus, Daniela
Kim, SeungJun
Date Issued
September 9, 2024
Journal
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher
ACM
Citation
Kim, Gwangbin, Hwang, Seokhyun, Seong, Minwoo, Yeo, Dohyeon, Rus, Daniela et al. 2024. "TimelyTale: A Multimodal Dataset Approach to Assessing Passengers' Explanation Demands in Highly Automated Vehicles." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8 (3).
Version
Final published version
Abstract
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 a lack 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3678544
