Developing Situational Awareness for Joint Action with Autonomous Vehicles

Unanswered questions about how human-AV interaction designers can support rider's informational needs hinders Autonomous Vehicles (AV) adoption. To achieve joint human-AV action goals - such as safe transportation, trust, or learning from an AV - sufficient situational awareness must be held by the human, AV, and human-AV system collectively. We present a systems-level framework that integrates cognitive theories of joint action and situational awareness as a means to tailor communications that meet the criteria necessary for goal success. This framework is based on four components of the shared situation: AV traits, action goals, subject-specific traits and states, and the situated driving context. AV communications should be tailored to these factors and be sensitive when they change. This framework can be useful for understanding individual, shared, and distributed human-AV situational awareness and designing for future AV communications that meet the informational needs and goals of diverse groups and in diverse driving contexts.

Learning Racing From an AI Coach: Effects of Multimodal Autonomous Driving Explanations on Driving Performance, Cognitive Load, Expertise, and Trust

In a pre-post experiment (n = 41), we test the impact of an AI Coach's explanatory communications modeled after the instructions of human driving experts. 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 directly compare how AI Coaching sessions employing these techniques impact driving performance, cognitive load, confidence, expertise, and trust in an observation learning context. Through interviews, we delineate the learning process of our participants. Results show that an AI driving coach can be useful for teaching performance driving skills to novices. Comparing between groups, we find the type and modality of information influences performance outcomes. We attribute differences to how information directed attention, mitigated uncertainty, and influenced overload experienced by participants. These, in turn, affected how successfully participants were able to learn. Results suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications that can instruct without overwhelming. Further, they support the need to align communications with human learning and cognitive processes. Results are synthesized into eight design implications for future autonomous vehicle HMI and AI coach design.

Explainable AI And Visual Reasoning: Insights From Radiology

Why do explainable AI (XAI) explanations in radiology, despite their promise of transparency, still fail to gain human trust? Current XAI approaches provide justification for predictions, however, these do not meet practitioners' needs. These XAI explanations lack intuitive coverage of the evidentiary basis for a given classification, posing a significant barrier to adoption. We posit that XAI explanations that mirror human processes of reasoning and justification with evidence may be more useful and trustworthy than traditional visual explanations like heat maps. Using a radiology case study, we demonstrate how radiology practitioners get other practitioners to see a diagnostic conclusion's validity. Machine-learned classifications lack this evidentiary grounding and consequently fail to elicit trust and adoption by potential users. Insights from this study may generalize to guiding principles for human-centered explanation design based on human reasoning and justification of evidence.