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<p>Modern autonomous driving algorithms in complex urban environments strive for safety, comfort, and intelligence, qualities exemplified by expert drivers. This paper presents a comprehensive EEG dataset comparing 10 expert and 10 novice drivers in 13 naturalistic urban driving conditions on a fixed 5.7-kilometer route in in a city. Our multi-modal dataset synchronizes brain activity with vehicle CAN bus data, traffic participant information, and psychophysiological measures (Electrodermal Activity and Heart Rate) from drivers. Uniquely, we also collected physiological and subjective feedback from two passengers per trip to validate driving performance quality. All participants (drivers and passengers)&#xa0;completed a series of standardized subjective questionnaires at pre- and post-experiment. Finally, they participated in post-drive semi-structured interviews exploring driver decision-making processes and passenger experience. This novel dataset enables researchers to decode the neural signatures underlying driving expertise, providing valuable insights for developing more human-like, intelligent autonomous driving algorithms that can better navigate the complexities of urban traffic environments.</p>

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An EEG dataset for understanding driving expertise from naturalistic urban road experiments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月16日

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  • Jiangtao Gong,
  • Yueteng Yu,
  • Yancheng Cao,
  • Ruoxuan Yang,
  • Xiang Chang,
  • Haoming Tang,
  • Xiaoji Zheng,
  • Yiyao Liu,
  • Shanhe You,
  • Chen Zheng,
  • Guyue Zhou

摘要

Modern autonomous driving algorithms in complex urban environments strive for safety, comfort, and intelligence, qualities exemplified by expert drivers. This paper presents a comprehensive EEG dataset comparing 10 expert and 10 novice drivers in 13 naturalistic urban driving conditions on a fixed 5.7-kilometer route in in a city. Our multi-modal dataset synchronizes brain activity with vehicle CAN bus data, traffic participant information, and psychophysiological measures (Electrodermal Activity and Heart Rate) from drivers. Uniquely, we also collected physiological and subjective feedback from two passengers per trip to validate driving performance quality. All participants (drivers and passengers) completed a series of standardized subjective questionnaires at pre- and post-experiment. Finally, they participated in post-drive semi-structured interviews exploring driver decision-making processes and passenger experience. This novel dataset enables researchers to decode the neural signatures underlying driving expertise, providing valuable insights for developing more human-like, intelligent autonomous driving algorithms that can better navigate the complexities of urban traffic environments.