BATON is a new multimodal naturalistic driving dataset and benchmark for predicting bidirectional control transitions between drivers and automation systems.
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4 Pith papers cite this work. Polarity classification is still indexing.
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cs.HC 4years
2026 4verdicts
UNVERDICTED 4roles
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WSTypist is a new RL-based simulation model that reproduces human-like word suggestion strategies, individual differences, and adaptation to design changes in mobile text entry.
The adaptive bounded-rationality model anticipates hazardous takeovers with better coverage and lead time than baselines while aligning inferred parameters with eye-tracking metrics.
Polite chatbot feedback lowers psychological reactance and boosts behavioral intentions but lacks engagement, whereas verbal leakage heightens surprise and engagement at the expense of increased reactance.
citing papers explorer
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BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving
BATON is a new multimodal naturalistic driving dataset and benchmark for predicting bidirectional control transitions between drivers and automation systems.
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Simulating Word Suggestion Usage in Mobile Typing to Guide Intelligent Text Entry Design
WSTypist is a new RL-based simulation model that reproduces human-like word suggestion strategies, individual differences, and adaptation to design changes in mobile text entry.
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Adaptive Bounded-Rationality Modeling of Early-Stage Takeover in Shared-Control Driving
The adaptive bounded-rationality model anticipates hazardous takeovers with better coverage and lead time than baselines while aligning inferred parameters with eye-tracking metrics.
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Polite But Boring? Trade-offs Between Engagement and Psychological Reactance to Chatbot Feedback Styles
Polite chatbot feedback lowers psychological reactance and boosts behavioral intentions but lacks engagement, whereas verbal leakage heightens surprise and engagement at the expense of increased reactance.