Scott Carter

schoolToyota Research Institutebar_chart#2244 Global Rank

Total Papers

HCI Rank

#2244

Research Areas

Years Active

Recent Publications

A Framework for Adapting In-Car Touchscreen Interfaces to Driver Behaviors, Perception, and Cognition

Although in-car touchscreens expand interaction possibilities, they risk compromising driver safety and vigilance. We propose a data- and expert-informed framework for designing adaptive touchscreens that respond to a driver’s usage profile and cognitive state, maximizing usability while mitigating safety risks. First…

UIST '255th+ AuthorHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS) +1

Touchscreens in Motion: Quantifying the Impact of Cognitive Load on Distracted Drivers

This study investigates the interplay between a driver's cognitive load, touchscreen interactions, and driving performance. Using an N-back task to induce four levels of cognitive load, we measured physiological responses (pupil diameter, electrodermal activity), subjective workload (NASA-TLX), touchscreen performance…

CHI '234th AuthorHonorable MentionEV Charging & Eco-Driving Interfaces +2

Save A Tree or 6 kg of CO2? Understanding Effective Carbon Footprint Interventions for Eco-Friendly Vehicular Choices

From ride-hailing to car rentals, consumers are often presented with eco-friendly options. Beyond highlighting a "green" vehicle and CO2 emissions, CO2 equivalencies have been designed to provide understandable amounts; we ask which equivalencies will lead to eco-friendly decisions. We conducted five ride-hailing scen…

CHI '232nd AuthorEV Charging & Eco-Driving Interfaces

Understanding People's Perception and Usage of Plug-in Electric Hybrids

Electrification is an important first step toward reducing the greenhouse emissions of passenger vehicles. However, how drivers drive, charge, and operate their electrified vehicles can have a large impact on their emissions, particularly for Plug-in Hybrid Electric vehicles (PHEVs) that combine all-electric driving w…

CHI '22CorrespondingHuman-LLM Collaboration +1

You Complete Me: Human-AI Teams and Complementary Expertise

People consider recommendations from AI systems in diverse domains ranging from recognizing tumors in medical images to deciding which shoes look cute with an outfit. Implicit in the decision process is the perceived expertise of the AI system. In this paper, we investigate how people trust and rely on an AI assistant…

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