For Authors

• (User interaction with or user interfaces for) green cars
• (User interaction with or user interfaces for) car sharing
• User perception, acceptance and trust in technologies related to sustainability, environmental impact
• User interfaces for behavior change towards sustainable mobility
• The “second life” of a car: what happens to car after the first owner sells it to another owner (potentially: from another country, context)
• Environmental impact of technologies related to AutomotiveUI
• Prosocial traffic behavior
• The effect of driving automation on employment

• Accessibility of AutomotiveUI related technology, settings, and contexts
• Under-represented groups and their experiences
• Technologies that accommodate more than “the average” user
• Experience in and perspective on AutomotiveUI for the Global South

AutomotiveUI technologies and interventions, and automated technology can change experience in various ways. This broad category captures aspects such as the relationship between AutomotiveUI and:

• Ethics (related to AutomotiveUI)
• Human and humane experiences (related to AutomotiveUI)
• Trust (related to AutomotiveUI)
• Ethical and social dilemma’s (related to AutomotiveUI)
• Philosophical perspectives (related to AutomotiveUI)
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• Design and validation of novel interfaces and artifacts
• Novel methods, insights from engaging in or with the design of interactive systems and artefacts.

• Areas where the field of “computational interaction” (see subcommittee description for CHI) intersects with AutomotiveUI
• For example: use of signal detection theory, statistics, control theory, natural language processing, machine learning, deep learning, cognitive architectures, simulation to gain insight into AutomotiveUI
• Computational cognitive or social models to predict human behavior for future interfaces
• Models or simulations to integrate insights about driving (e.g. in cognitive architectures) 

We encourage submissions that take a radically different (“out of the box”) perspective compared to previous AutomotiveUI publications, provided that this perspective is relevant for the AutomotiveUI community and shows scientific rigour and clarity. For example, papers that apply different methods, or that test an idea that goes against popular opinion. This category is not meant to cover what CHI calls “Alt-CHI” (as in: work that is hard to get into CHI). Rather, it encourages different perspectives that are scientifically valid and grounded, but that are in a substantial way different from the “typical” AutomotiveUI paper.

• Different input modalities, such as multi-modal, speech, audio, gestural, thermal, touch, natural input/output
• Different output modalities, such as multi-modal, audio, gestural, thermal, touch, natural input/output
• In-car gaming, entertainment and social experiences
• Interfaces for navigation
• Text input and output while driving
• Applications and user-interfaces for inter-vehicle communication
• Sensors and context for interactive experiences in the car
• Biometrics and physiological sensors as a user interface component
• Electric vehicle interfaces
• Affective intelligent interfaces
• Future interfaces and technology for the automotive domain

• Automated driving and interfaces for (semi) autonomous driving
• Head-Up Displays (HUDs) and Augmented Reality (AR) concepts
• Cooperative Driving/Connected Vehicles
• Assistive technology in the vehicular context
• Information access (search, browsing, etc.)
• Vehicle-based apps, web/cloud enabled connectivity
• Entertainment and play (semi) autonomous driving
• Ethics

• Methods and tools for automotive user-interface research, including simulation
• Automotive user-interface frameworks and toolkits
• Naturalistic/field studies of automotive user interfaces
• Automotive user-interface standards
• Modeling techniques for cognitive workload and visual demand estimation

• Human cognition and behavior in automotive settings
• Different user groups and user group characteristics
• Subliminal cues, warnings and feedback to augment driving behavior
• Emotional or cognitive state recognition while driving
• Detecting / measuring driver distraction and workload
• Detecting and estimating user intentions
• (Cognitive or social) Modeling of driver though, behavior, and experience