Effects of Multimodal Explanations for Autonomous Driving on Driving Performance, Cognitive Load, Expertise, Confidence, and Trust
arxiv(2024)
摘要
Advances in autonomous driving provide an opportunity for AI-assisted driving
instruction that directly addresses the critical need for human driving
improvement. How should an AI instructor convey information to promote
learning? In a pre-post experiment (n = 41), we tested the impact of an AI
Coach's explanatory communications modeled after performance driving expert
instructions. Participants were divided into four (4) groups to assess two (2)
dimensions of the AI coach's explanations: information type ('what' and
'why'-type explanations) and presentation modality (auditory and visual). We
compare how different explanatory techniques impact driving performance,
cognitive load, confidence, expertise, and trust via observational learning.
Through interview, we delineate participant learning processes. Results show AI
coaching can effectively teach performance driving skills to novices. We find
the type and modality of information influences performance outcomes.
Differences in how successfully participants learned are attributed to how
information directs attention, mitigates uncertainty, and influences overload
experienced by participants. Results suggest efficient, modality-appropriate
explanations should be opted for when designing effective HMI communications
that can instruct without overwhelming. Further, results support the need to
align communications with human learning and cognitive processes. We provide
eight design implications for future autonomous vehicle HMI and AI coach
design.
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