When2Talk: When Should a Proactive In-Car Agent Talk?
Kaiser Hamid, Peihang Li, Nade Liang
Abstract
Proactive in-cabin agents can help passengers understand automated-vehicle (AV) behavior, but communicating every ride event may introduce unnecessary interruptions. We investigated how communication should adapt to event priority and passenger activity. In a mixed-methods within-subject study, 41 participants rode as passenger in a VR simulated fully-automated vehicle. We compared an event-triggered (ET) policy that communicated immediately at every event with a context-sensitive (CS) policy that selected Immediate, Delayed, or Silent communications. CS increased communication appropriateness and substantially reduced perceived interruption. Perceived trust did not differ between policies, although baselines dispositional trust differentiated communication preferences. Findings highlight event consequence, passenger activity, continuing information value, and confirmation need as key considerations for selective in-cabin communication.
Create a lesson
Related papers
From Review to Reuse: How Post-Task Workflow Can Support Human-AI Agent Interaction
Zekun Wu, Xinru Wang, Rock Yuren Pang et al.
Middleware for Feed Recommendation in Practice: How Feed Creators Build, Maintain, and Sustain Custom Feeds on Bluesky
Tony Zhou, Leijie Wang, Amy X. Zhang
NeuroClick: Preserving Surgeon Autonomy through Hands-Free Earable Tooth-Click Control in Neurosurgery
Jonas Hummel, Maximilian Burzer, Clara Sayffaerth et al.
Understanding Game Coaching on Gig Platforms
Hwijoon Lee, Saiph Savage
Reconstruction and Reflection of Positive Experiences through Resurfacing Laughter-indexed Everyday Moments
Jun Fang, Jiajin Li, Yuntao Wang et al.
TraceMind: Predicting User Information Uptake from Low-Cost Interaction Traces during Human-LLM Content Co-Generation
Yu Mei, Fengyou Zu, Ruiwen Zhang et al.