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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

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  • Bo Chai,
  • Mingyuan Zhang,
  • Meichen Liu,
  • Le Fang,
  • Ziyi Chen,
  • Wei Gong,
  • Shuting Jin,
  • Gokula Manikandan Senthil Kumar,
  • Xingtong Chen,
  • Stephen J. Wang

摘要

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.