Wei-Hsiang Lo | Searcharxiv

arXiv2026

Multimodal Takeover Requests for Drivers with Hearing Loss: Implications for AI-Enabled Communication in Automated Vehicles

More than 430 million people worldwide live with disabling hearing loss. Although people with hearing loss are legally permitted to drive and may benefit from conditionally automated vehicles, SAE Level 3 systems still require drivers to respond to takeover requests when automation reaches its limits. Existing takeover requests often rely on auditory information, yet little evidence addresses visual and tactile designs for drivers who cannot rely on sound. This driving-simulator study with 40 participants examined the effects of information type (instructional, informative, and baseline), signal type (visual, tactile, and visual-tactile), and hearing condition (normal hearing and simulated hearing impairment) on takeover performance. Information type significantly affected reaction time, with baseline displays producing the shortest times. Signal type significantly affected reaction and takeover time, with visual-tactile displays producing the shortest times. The interaction between signal type and information type was significant for all three measures. Visual-tactile displays produced the shortest reaction times within every information type. With visual-tactile signaling, simple baseline alerts prompted the fastest reactions and the most abrupt maneuvers, whereas informative content produced the lowest mean maximum resulting acceleration. Hearing condition showed no significant main effect on any measure. These findings suggest that AI-enabled vehicles can support urgent takeover communication through visual-tactile displays and can adapt message content to the time available and the maneuver quality required, with implications for drivers across hearing abilities.

cs.HC↗

arXiv2026

Toward AI standardization: A triadic human-ai collaboration framework for multi-level autonomous mobility

The goal of the current study is to introduce a triadic human-AI collaboration framework that could be applied in transportation systems such as automated vehicles, micromobility systems, and vehicle teleoperation. Previous standards, such as SAE Levels of Automation, have focused on defining automation levels based on who controls the vehicle. However, it is still not clear how human users and AI should collaborate in real time, especially in dynamic driving contexts where roles can shift frequently. To fill this gap, this study proposed a triadic human-AI collaboration framework with three AI roles: Advisor, Co-Pilot, and Guardian. These roles can dynamically adapt to human needs based on real-time data, such as mental states and environmental conditions. The Advisor AI offers informational support without direct intervention. The Co-Pilot AI provides partial intervention when needed, with the goal of sharing control with humans. The Guardian AI performs emergency overrides if necessary. The use cases for these AI roles in micromobility devices, such as e-scooters, are presented to demonstrate how these roles can influence user preferences and trust. Overall, the study takes a first step toward a universal role-based collaborative framework for AI standardization and explores how AI technologies can be embedded in future transportation systems while considering human interactions.

cs.HC↗