Evaluating In-Car Tasks’ Distraction Effects with Drive-In Lab

Kujala, Tuomo; Sarkar, A. M. Jehad · 2025 · openalex

DOI: 10.1145/3706598.3713590

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the lack of valid and reliable methods for measuring driver distraction caused by in-car user interfaces (UIs). Existing laboratory metrics, such as off-road glance durations and detection response tasks, often lack ecological validity and fail to establish a causal link to real-world crash risk. To resolve this, the authors introduce the "Drive-In Lab," a facility where real passenger cars are connected to a driving simulation, allowing for the assessment of distraction effects on cognitive processes crucial for safe headway maintenance. The study utilized two 2024 electric vehicle models (VW ID.7 and Kia EV9) with 32 participants per car model, recruited across four age groups to ensure demographic representativeness. The Drive-In Lab features a 180-degree front projection screen that provides realistic visual looming cues of a lead vehicle. Participants performed a longitudinal headway maintenance task in a simulated car-following scenario where the lead car made unpredictable accelerations and decelerations. Distraction was operationalized by comparing drivers’ headway maintenance during ten specific in-car tasks against a baseline driving condition. The method accounted for individual differences by measuring each participant’s brake response time and visual search speed prior to the driving trials. The results demonstrated that the proposed method is a valid and reliable tool for differentiating distraction effects. It successfully distinguished between cars, specific in-car tasks, and individual drivers, categorizing effects on crash potential as large, medium, small, or negligible. The study identified unintuitive and distracting features in the UI designs of the tested vehicles, revealing which tasks significantly increased crash potential compared to baseline driving. Furthermore, the method proved capable of identifying which in-car activities could be safely performed while driving and which should be restricted, particularly in the absence of driving assistance systems like adaptive cruise control. The significance of this work lies in providing a benchmark for in-car UI design that is grounded in causal inference regarding crash potential rather than abstract statistical associations. By focusing on worst-case scenarios and realistic visual cues, the Drive-In Lab offers a more ecologically valid approach to evaluating distraction. This method supports the development of safer in-car interactions by revealing the specific cognitive demands of UI tasks and their direct impact on a driver’s ability to maintain safe distances, thereby addressing critical gaps in current regulatory guidelines and HCI research standards.

Key finding

The Drive-In Lab method reliably differentiates the distraction effects of in-car tasks on crash potential, providing valid estimates of safety risks that existing glance-based or artificial task metrics fail to capture.

Methodology

simulator

Sample size: 64

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• visual
• distraction detection algorithms
• mind wandering
• visual occlusion
• external distraction
• manual

Information type

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• Methodological Resource: measurement protocol, tool software
• Theoretical Contribution: conceptual framework