Investigating the impact of in-vehicle warning information complexity on drivers: The role of working memory capacity and cognitive load

Abstract

In-vehicle warning systems significantly reduce collisions. However, poorly designed warnings, such as those with excessive or insufficient information, intensify the resource consumption of the drivers. The working memory is a crucial component of the cognitive function, which is closely related to the processing of short-term information. Therefore, this paper investigates the impact of the complexity of the warning messages on the behavior and physiological states of the driver, taking into account individual differences in working memory capacity and cognitive load levels. A total of 37 participants are recruited to conduct a 4 (warning information complexity) × 2 (working memory capacity) × 2 (cognitive load) mixed design driving simulation experiment, with working memory capacity treated as a between-subjects factor. An eye-tracker and a physiometer are employed to record participant’s visual motion and heart rate. A correlation analysis is then conducted to identify key dependent variables, and a Generalized Linear Mixed-effects Model (GLMM), which considers random effects, is used to analyze the impact of each experimental factor on the drivers. The obtained results demonstrate that visually rich warnings lead to increased braking reaction times, especially between drivers having low working memory capacity and under high cognitive load. Although detailed warnings are easier to understand, they tend to reduce Root Mean Square of Successive Differences (RMSSD) of the driver under higher cognitive loads, indicating increased tension and annoyance. In addition, the combination of visually simple and auditorily rich warnings has significant advantages, allowing almost all types of participants to perceive risks more quickly, which significantly reduces the collision risks. These findings offer theoretical insights to assist manufacturers in designing human-centered, personalized, and adaptive in-vehicle warning systems.