Existing measurements of driver distraction in laboratory settings lack construct and ecological validity, and therefore, cannot provide reliable estimates of in-car tasks’ distraction effects. In this paper, we operationalize driver distraction in a novel way with the help of Drive-In Lab, where any passenger car can be connected to a driving simulation. The operationalization is based on drivers’ headway maintenance during in-car tasks as compared to baseline driving, while accommodating situational and driver-specific variables, such as brake response times. Realistic visual looming cues enable evaluation of distraction effects on cognitive processes crucial for safe driving. Validation studies with two 2024 car models indicate that the method can reliably differentiate distraction effects between cars, in-car tasks, and drivers as large, medium, small, or no effect on crash potential. The method supports design of in-car interactions by providing valid means to reveal the worst and best practices in in-car user interface design.
https://dl.acm.org/doi/10.1145/3706598.3713590
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)