Designing Explainable In-vehicle Agents for Conditionally Automated Driving: A Holistic Examination with Mixed Method Approaches
| dc.contributor.author | Wang, Manhua | en |
| dc.contributor.committeechair | Jeon, Myounghoon | en |
| dc.contributor.committeemember | Patrick, Rafael | en |
| dc.contributor.committeemember | Zhang, Yiqi | en |
| dc.contributor.committeemember | Klauer, Sheila G. | en |
| dc.contributor.department | Industrial and Systems Engineering | en |
| dc.date.accessioned | 2024-08-17T08:00:20Z | en |
| dc.date.available | 2024-08-17T08:00:20Z | en |
| dc.date.issued | 2024-08-16 | en |
| dc.description.abstract | Automated vehicles (AVs) are promising applications of artificial intelligence (AI). While human drivers benefit from AVs, including long-distance support and collision prevention, we do not always understand how AV systems function and make decisions. Consequently, drivers might develop inaccurate mental models and form unrealistic expectations of these systems, leading to unwanted incidents. Although efforts have been made to support drivers' understanding of AVs through in-vehicle visual and auditory interfaces and warnings, these may not be sufficient or effective in addressing user confusion and overtrust in in-vehicle technologies, sometimes even creating negative experiences. To address this challenge, this dissertation conducts a series of studies to explore the possibility of using the in-vehicle intelligent agent (IVIA) in the form of the speech user interface to support drivers, aiming to enhance safety, performance, and satisfaction in conditionally automated vehicles. First, two expert workshops were conducted to identify design considerations for general IVIAs in the driving context. Next, to better understand the effectiveness of different IVIA designs in conditionally automated driving, a driving simulator study (n=24) was conducted to evaluate four types of IVIA designs varying by embodiment conditions and speech styles. The findings indicated that conversational agents were preferred and yielded better driving performance, while robot agents caused greater visual distraction. Then, contextual inquiries with 10 drivers owning vehicles with advanced driver assistance systems (ADAS) were conducted to identify user needs and the learning process when interacting with in-vehicle technologies, focusing on interface feedback and warnings. Subsequently, through expert interviews with seven experts from AI, social science, and human-computer interaction domains, design considerations were synthesized for improving the explainability of AVs and preventing associated risks. With information gathered from the first four studies, three types of adaptive IVIAs were developed based on human-automation function allocation and investigated in terms of their effectiveness on drivers' response time, driving performance, and subjective evaluations through a driving simulator study (n=39). The findings indicated that although drivers preferred more information provided to them, their response time to road hazards might be degraded when receiving more information, indicating the importance of the balance between safety and satisfaction. Taken together, this dissertation indicates the potential of adopting IVIAs to enhance the explainability of future AVs. It also provides key design guidelines for developing IVIAs and constructing explanations critical for safer and more satisfying AVs. | en |
| dc.description.abstractgeneral | Automated vehicles (AVs) are an exciting application of artificial intelligence (AI). While these vehicles offer benefits like helping with long-distance driving and preventing accidents, people often do not understand how they work or make decisions. This lack of understanding can lead to unrealistic expectations and potentially dangerous situations. Even though there are visual and sound alerts in these cars to help drivers, they are not always sufficient to prevent confusion and over-reliance on technology, sometimes making the driving experience worse. To address this challenge, this dissertation explores the use of in-vehicle intelligent agents (IVIAs), in the form of speech assistant, to help drivers better understand and interact with AVs, aiming to improve safety, performance, and overall satisfaction in semi-automated vehicles. First, two expert workshops helped identify key design features for IVIAs. Then, a driving simulator study with 24 participants tested four different designs of IVIAs varying in appearance and how they spoke. The results showed that people preferred conversational agents, which led to better driving behaviors, while robot-like agents caused more visual distractions. Then, through contextual inquiries with 10 drivers who own vehicles with advanced driver assistance systems (ADAS), I identified user needs and how they learn to interact with in-car technologies, focusing on feedback and warnings. Subsequently, I conducted expert interviews with seven professionals from AI, social science, and human-computer interaction fields, which provided further insights into facilitating the explainability of AVs and preventing associated risks. With the information gathered, three types of adaptive IVIAs were developed based on whether the driver was actively in control of the vehicle, or the driving automation system was in control. The effectiveness of these agents was evaluated through drivers' brake and steer response time, driving performance, and user satisfaction through another driving simulator study with 39 participants. The findings indicate that although drivers appreciated more detailed explanations, their response time to road hazards slowed down, highlighting the need to balance safety and satisfaction. Overall, this research shows the potential of using IVIAs to make AVs easier to understand and safer to use. It also offers important design guidelines for creating these IVIAs and their speech contents to improve the driving experience. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41245 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120951 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Explainable AI | en |
| dc.subject | Automated Vehicles | en |
| dc.subject | Transparency | en |
| dc.subject | In-Vehicle Intelligent Agent | en |
| dc.subject | Human-Machine Interface | en |
| dc.title | Designing Explainable In-vehicle Agents for Conditionally Automated Driving: A Holistic Examination with Mixed Method Approaches | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Industrial and Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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