Visual-Attribute-Based Emotion Regulation of Angry Driving Behaviors

Abstract

Driver anger has become a severe transportation problem resulting in significant injuries and fatalities. The rapid development of intelligent transportation systems has provided new opportunities for dynamic angry driving regulations. However, the regulation qualities of different visual attributes under different parameters are not yet clear. In this article, we investigate the anger regulation quality of different parameters for different visual attributes during driving (color: cold/warm; symbol: flat/simulated; expression: positive/negative). Twenty-one drivers drove nine times (<i>N</i> = 189) on a simulated highway scene with the data recording. The regulation quality of the drivers&#x2019; anger was analyzed from their subjective experience, behavior, and physiology. Results indicate that regulation driving with different visual attributes presented better driver anger regulation quality compared with baseline driving. Additionally, for the color attribute, the regulation quality of the cold hue was better than the warm hue, and for the expression attribute, the positive expression was better than the negative expression. Our results provide preliminary insights for three specific visual attributes of a driver&#x2019;s anger regulation and compare their corresponding regulation qualities under different parameter changes. Our research provides empirical evidence for choosing different parameters among the three specific visual attributes in the early stages of designing a visual human&#x2013;machine interface for driver anger.