{
  "id": "hwang2026adaptivecartouchscreen",
  "type": "paper-conference",
  "title": "A Framework for Adapting In-Car Touchscreen Interfaces to Driver Behaviors, Perception, and Cognition",
  "author": [
    {
      "family": "Hwang",
      "given": "Seokhyun"
    },
    {
      "family": "Shen",
      "given": "Xiyuan"
    },
    {
      "family": "Filipowicz",
      "given": "Alexandre L. S."
    },
    {
      "family": "Best",
      "given": "Andrew"
    },
    {
      "family": "Costa",
      "given": "Jean"
    },
    {
      "family": "Carter",
      "given": "Scott"
    },
    {
      "family": "Fogarty",
      "given": "James"
    },
    {
      "family": "Wobbrock",
      "given": "Jacob O."
    }
  ],
  "issued": {
    "date-parts": [
      [
        2026,
        4,
        13
      ]
    ]
  },
  "container-title": "Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems",
  "page": "1-23",
  "DOI": "10.1145/3772318.3790434",
  "URL": "https://doi.org/10.1145/3772318.3790434",
  "abstract": "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, in a driving simulator study, we find that cognitive load slows touchscreen button selections by 20% and produced shorter, more frequent off-road glances. We also find that enlarging buttons improves selection speeds by 0.3 seconds but at the cost of requiring more display pages. Next, these findings informed a co-design session with expert in-cabin designers, generating guidelines for adaptive interfaces that balance usability and safety. These guidelines form the basis of our Profile-State Adaptive (PSA) framework, which integrates driver profiles with cognitive states to guide interface adaptations. We then extend the framework to include a quantitative Time-Cost model as well as design patterns for adaptive layouts across usage profiles and cognitive demands.",
  "keyword": "Cognition, Human-AI Interaction, Automotive, Conference, FA"
}