University of California - San Diego, La Jolla, CA, 92093, USA.
Toyota Research Institute, Los Altos, 94022, USA.
Sci Rep. 2024 Jun 6;14(1):13061. doi: 10.1038/s41598-024-62052-9.
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.
自动驾驶技术的进步为 AI 辅助驾驶指导提供了机会，这直接解决了人类驾驶改进的关键需求。AI 教练应该如何传达信息以促进学习？在一项前后测实验（n = 41）中，我们测试了模仿驾驶专家指导的 AI 教练解释性沟通对学习的影响。参与者被分为四组，评估 AI 教练解释的两个维度：信息类型（“是什么”和“为什么”类型的解释）和呈现方式（听觉和视觉）。我们通过观察学习来比较不同的解释技术如何影响驾驶表现、认知负荷、信心、专业知识和信任。通过访谈，我们描述了参与者的学习过程。结果表明，AI 教练可以有效地向新手传授驾驶技能。我们发现信息的类型和呈现方式会影响表现结果。参与者学习效果的差异归因于信息如何引导注意力、减轻不确定性以及影响参与者的过载。结果表明，在设计能够进行指导而不造成负担的有效 HMI 通信时，应选择高效、适合模式的解释。此外，结果支持根据人类学习和认知过程来调整沟通的必要性。我们为未来的自动驾驶汽车 HMI 和 AI 教练设计提供了八项设计启示。
