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会议信息
AutomotiveUI 2025: International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications
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会议日期:
2025-09-20
会议地点:
Brisbane, Australia
届数:
17
浏览: 14598   关注: 1   参加: 1

征稿
AutomotiveUI (or short: AutoUI) is the International ACM SIGCHI Conference on Automotive User Interfaces and Interactive Vehicular Applications. It is the premier forum for UI research in the automotive domain. The conference annually brings together over 200 researchers and practitioners interested in both the technical and the human aspects of in-vehicle user interfaces and applications, to provide a forum for the exchange of technical information concerning research (and practice) and educational activities for motor vehicle user interface development. We have multiple meeting categories in which researchers, practitioners, and other interested parties can take part in our conference and community. We welcome you to engage with us in this exciting field!
最后更新 Dou Sun 在 2024-11-24
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2025Reconfigurable Roadways: Envisioning the Future of V2X-Driven Autonomous Urban Infrastructure through an Interactive Tabletop Simulator
2025Self-supervised Learning for Detecting Local Contextual Anomalous Gaze Patterns of Individual Drivers in Level 3 Automated Driving
2025Enhancing Cyclist Safety in the EU: A Study on Lateral Overtaking Distance Across Seven Scenarios Using Lab and Crowdsourced Methods
2025Pedestrian Planet: What YouTube Driving from 233 Countries and Territories Teaches Us About the World
2025SOH Illusion: Misunderstandings of EV Battery State of Health and Methods to Promote Understanding
2024What Characterizes "Situations" in Situation Awareness? Findings from a Human-centered Investigation
2024Improving Driver Engagement with Level 2 Automated Systems: The Impact of Fully Shared Longitudinal Control
2024Text a Bit Longer or Drive Now? Resuming Driving after Texting in Conditionally Automated Cars
2024Multimodal Feedback for Effective Takeover in Automated Vehicles for Hearing Impairment
2024Towards Instrumented Fingerprinting of Urban Traffic: A Novel Methodology using Distributed Mobile Point-of-View Cameras
2024‘Talking with your Car’: Design of Human-Centered Conversational AI in Autonomous Vehicles
2024Driven to Distraction: Exploring Mind Wandering During a Virtual Reality City Drive
2024CARSI II: A Context-Driven Intelligent User Interface
2024GrokWalks: A Portable Virtual Reality Platform to Facilitate Studying Driver-Pedestrian Interactions
2023Designing Virtual Agent Human-Machine Interfaces Depending on the Communication and Anthropomorphism Levels in Augmented Reality
2023How to Make Reading in Fully Automated Vehicles a Better Experience? Effects of Active Seat Belt Retractions and a 2-Step Driving Profile on Subjective Motion Sickness, Ride Comfort and Acceptance
2023Communication of Uncertainty Information in Cooperative, Automated Driving: A Comparative Study of Different Modalities
2023Pimp My Ride: Designing Versatile eHMIs for Cyclists
2023Development of a Perceived Security Scale for Shared Automated Vehicles (PSSAV) and its Validation in Colombia and Germany
2022Together in the Car: A Comparison of Five Concepts to Support Driver-Passenger Collaboration
2021Conception, Development and First Evaluation of a Context-Adaptive User Interface for Commercial Vehicles
2021Investigating the Interplay between eHMI and dHMI for Automated Buses: How Do Contradictory Signals Influence a Pedestrian's Willingness to Cross?
2021Eye-Gaze Analysis of HUD Interventions for Conditional Automation to Increase Situation Awareness
2021Web-based Simulator for Studying Shared Control of Autonomous Platoons
2017Differentiating Cognitive Load Using a Modified Version of AttenD
2017Using EEG to Understand why Behavior to Auditory In-vehicle Notifications Differs Across Test Environments
2017Using EEG to Understand why Behavior to Auditory In-vehicle Notifications Differs Across Test Environments
2017Did You See Me?: Assessing Perceptual vs. Real Driving Gains Across Multi-Modal Pedestrian Alert Systems
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HaiLong Liu (599)
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HaiLong Liu (599)2020
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