A Multimodal Dataset of Psychological, Physiological, and Behavioral Responses in Diverse Driving Scenarios

Driver emotion significantly impacts traffic safety, driving behavior, and driving experience, often being directly influenced by different driving scenarios. To explore this relationship, we present EmoRoad, a multimodal dataset capturing emotional, physiological, and behavioral responses under diverse driving scenarios. These scenarios are defined by three dimensions, each with two conditions: road scenario (urban/suburban), traffic density (jam/flow), and weather (sunny/rainy). The combination of these factors results in eight representative driving scenarios. Data were collected from 50 participants (30 female, 20 male; aged 18-67), including first-person driving videos, facial videos, EEG signals, eye-tracking data, steering wheel touch data, vehicle dynamics, and emotion annotations. EmoRoad offers a rich resource for research on emotion recognition, behavior modeling, and the effects of driving context on emotion, with potential applications in intelligent transportation systems and affective computing.