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作者:

Mou, Luntian (Mou, Luntian.) | Zhao, Yiyuan (Zhao, Yiyuan.) | Zhou, Chao (Zhou, Chao.) | Nakisa, Bahareh (Nakisa, Bahareh.) | Rastgoo, Mohammad Naim (Rastgoo, Mohammad Naim.) | Ma, Lei (Ma, Lei.) | Huang, Tiejun (Huang, Tiejun.) | Yin, Baocai (Yin, Baocai.) (学者:尹宝才) | Jain, Ramesh (Jain, Ramesh.) | Gao, Wen (Gao, Wen.)

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EI Scopus SCIE

摘要:

Negative emotions may induce dangerous driving behaviors leading to extremely serious traffic accidents. Therefore, it is necessary to establish a system that can automatically recognize driver emotions so that some actions can be taken to avoid traffic accidents. Existing studies on driver emotion recognition have mainly used facial data and physiological data. However, there are fewer studies on multimodal data with contextual characteristics of driving. In addition, fully fusing multimodal data in the feature fusion layer to improve the performance of emotion recognition is still a challenge. To this end, we propose to recognize driver emotion using a novel multimodal fusion framework based on convolutional long-short term memory network (ConvLSTM), and hybrid attention mechanism to fuse non-invasive multimodal data of eye, vehicle, and environment. In order to verify the effectiveness of the proposed method, extensive experiments have been carried out on a dataset collected using an advanced driving simulator. The experimental results demonstrate the effectiveness of the proposed method. Finally, a preliminary exploration on the correlation between driver emotion and stress is performed.

关键词:

Attention mechanism driver emotion recognition Physiology convolutional long short term memory multimodal fusion Emotion recognition Accidents driver stress Vehicles Feature extraction Data mining Anxiety disorders

作者机构:

  • [ 1 ] [Mou, Luntian]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 2 ] [Zhao, Yiyuan]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 3 ] [Zhou, Chao]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 4 ] [Yin, Baocai]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 5 ] [Nakisa, Bahareh]Deakin Univ, Fac Sci Engn & Built Environm, Sch Informat Technol, Burwood, Vic 3125, Australia
  • [ 6 ] [Rastgoo, Mohammad Naim]Queensland Univ Technol, Sch Elect Engn & Comp Sci, Brisbane, Qld 4000, Australia
  • [ 7 ] [Ma, Lei]Beijing Acad Artificial Intelligence, Beijing 100875, Peoples R China
  • [ 8 ] [Huang, Tiejun]Beijing Acad Artificial Intelligence, Beijing 100875, Peoples R China
  • [ 9 ] [Ma, Lei]Peking Univ, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China
  • [ 10 ] [Huang, Tiejun]Peking Univ, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China
  • [ 11 ] [Jain, Ramesh]Univ Calif Irvine, Inst Future Hlth, Bren Sch Informat & Comp Sci, Irvine, CA 92697 USA
  • [ 12 ] [Gao, Wen]Peking Univ, Inst Digital Media, Beijing 100871, Peoples R China
  • [ 13 ] [Gao, Wen]Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen 518055, Guangdong, Peoples R China

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来源 :

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING

ISSN: 1949-3045

年份: 2023

期: 4

卷: 14

页码: 2970-2981

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 35

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

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