Technical Report 2025This report presents findings from a comprehensive survey of commercial fleet professionals involved in fleet safety (specifically managers, coaches and decision-makers) regarding the perceived effectiveness, practical implementation, and strategic value of different driver coaching approaches. Grounded in the broader objective of improving safety outcomes through evidence-based coaching strategies, the survey sought to explore both individual preferences and organizational practices.The survey respondents consisted of two distinct groups: individuals who identify coaching as a key part of their primary job, and commercial dashcam decision-makers who do not directly engage in driver coaching.Technical report, not peer reviewed.
UIST Adjunct 2025Using head-mounted Virtual Reality (VR) displays to simulate driving is critical to study driving behavior and design driver assistance systems. But existing VR driving simulators are often limited to tracking only eye movements.
Technical Report 2025This report critically evaluates coaching methodologies for enhancing fleet driver safety, engagement, and overall organizational performance. Drawing on empirical research across education, behavioral science, and fleet management, this analysis identifies key dimensions of effective commercial driver coaching, highlights significant limitations of current practices, and outlines strategic recommendations focused explicitly on differentiation and optimization.Technical report, not peer reviewed.
CHI 2025
Trust in autonomous vehicles varies widely among individuals, and this study uses machine learning to identify the key factors influencing young adults’ trust. Surveying over 1,400 participants, the analysis reveals that perceptions of AV risks and benefits, usability attitudes, institutional trust, prior experience, and mental models are the strongest predictors of trust—while psychosocial traits and driving styles play a lesser role. These findings underscore the need to account for individual differences when designing trustworthy AV systems.
CHI 2025
Explanation errors from autonomous vehicles undermine user comfort, trust, and satisfaction—particularly in unfamiliar or non-routine driving contexts. Through a driving simulator study, the work shows that even subtle inaccuracies in how AVs communicate can erode user confidence, emphasizing the need for clear, context-aware explanations to foster reliable human-machine interaction.