What and When to Explain? On-road Evaluation of Explanations in Highly Automated Vehicles
Author(s)
Kim, Gwangbin
Yeo, Dohyeon
Jo, Taewoo
Rus, Daniela
Kim, SeungJun
Date Issued
September 27, 2023
Journal
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher
ACM
Citation
Kim, Gwangbin, Yeo, Dohyeon, Jo, Taewoo, Rus, Daniela and Kim, SeungJun. 2023. "What and When to Explain? On-road Evaluation of Explanations in Highly Automated Vehicles." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7 (3).
Version
Final published version
Abstract
Explanations in automated vehicles help passengers understand the vehicle’s state and capabilities, leading to increased trust in the technology. Specifically, for passengers of SAE Level 4 and 5 vehicles who are not engaged in the driving process, the enhanced sense of control provided by explanations reduces potential anxieties, enabling them to fully leverage the benefits of automation. To construct explanations that enhance trust and situational awareness without disturbing passengers, we suggest testing with people who ultimately employ such explanations, ideally under real-world driving conditions. In this study, we examined the impact of various visual explanation types (perception, attention, perception+attention) and timing mechanisms (constantly provided or only under risky scenarios) on passenger experience under naturalistic driving scenarios using actual vehicles with mixed-reality support. Our findings indicate that visualizing the vehicle’s perception state improves the perceived usability, trust, safety, and situational awareness without adding cognitive burden, even without explaining the underlying causes. We also demonstrate that the traffic risk probability could be used to control the timing of an explanation delivery, particularly when passengers are overwhelmed with information. Our study’s on-road evaluation method offers a safe and reliable testing environment and can be easily customized for other AI models and explanation modalities.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3610886
