Human-Readable Machine Intent: Designing Visual And Auditory Interfaces For Autonomous Systems

Abstract

As autonomous systems increasingly integrate into public spaces, conveying machine intent to humans has emerged as a critical safety and usability concern. This article examines multimodal approaches to machine-to-human communication, with focus on visual and auditory interfaces that effectively bridge the gap between algorithmic decision-making and human understanding. Findings from extensive user testing demonstrate significant improvements in human-machine interaction when adaptive intent displays are implemented. A comprehensive framework for developing inclusive intent communication systems accommodates diverse user needs, including those with sensory impairments. The work underscores the necessity of transparent machine intent as a foundational element in fostering trust and safety in human-autonomous system interactions.