Intelligent emotion recognition for drivers using model-level multimodal fusion
Unstable emotions are considered to be an important factor contributing to traffic accidents. The probability of accidents can be reduced if emotional anomalies of drivers can be quickly identified and intervened. In this paper, we present a multimodal emotion recognition model, MHLT, which performs...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1599428/full |
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| _version_ | 1849427930045743104 |
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| author | Xing Luan Quan Wen Bo Hang |
| author_facet | Xing Luan Quan Wen Bo Hang |
| author_sort | Xing Luan |
| collection | DOAJ |
| description | Unstable emotions are considered to be an important factor contributing to traffic accidents. The probability of accidents can be reduced if emotional anomalies of drivers can be quickly identified and intervened. In this paper, we present a multimodal emotion recognition model, MHLT, which performs model-level fusion through an attentional mechanism. By integrating video and audio modalities, the accuracy of emotion recognition is significantly improved. And the model performs better in predicting emotion intensity, a driver emotion recognition dimension, than traditional results that focus more on emotion, recognition classification. |
| format | Article |
| id | doaj-art-74ee4fe76aef4effb928a16006c0667f |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-74ee4fe76aef4effb928a16006c0667f2025-08-20T03:28:52ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-07-011310.3389/fphy.2025.15994281599428Intelligent emotion recognition for drivers using model-level multimodal fusionXing Luan0Quan Wen1Bo Hang2College of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaHubei University of Arts and Science, Xiangyang, ChinaUnstable emotions are considered to be an important factor contributing to traffic accidents. The probability of accidents can be reduced if emotional anomalies of drivers can be quickly identified and intervened. In this paper, we present a multimodal emotion recognition model, MHLT, which performs model-level fusion through an attentional mechanism. By integrating video and audio modalities, the accuracy of emotion recognition is significantly improved. And the model performs better in predicting emotion intensity, a driver emotion recognition dimension, than traditional results that focus more on emotion, recognition classification.https://www.frontiersin.org/articles/10.3389/fphy.2025.1599428/fullroad rage detectiondriver emotion recognitionmultimodal emotion recognitionattention mechanismdeep learning |
| spellingShingle | Xing Luan Quan Wen Bo Hang Intelligent emotion recognition for drivers using model-level multimodal fusion Frontiers in Physics road rage detection driver emotion recognition multimodal emotion recognition attention mechanism deep learning |
| title | Intelligent emotion recognition for drivers using model-level multimodal fusion |
| title_full | Intelligent emotion recognition for drivers using model-level multimodal fusion |
| title_fullStr | Intelligent emotion recognition for drivers using model-level multimodal fusion |
| title_full_unstemmed | Intelligent emotion recognition for drivers using model-level multimodal fusion |
| title_short | Intelligent emotion recognition for drivers using model-level multimodal fusion |
| title_sort | intelligent emotion recognition for drivers using model level multimodal fusion |
| topic | road rage detection driver emotion recognition multimodal emotion recognition attention mechanism deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1599428/full |
| work_keys_str_mv | AT xingluan intelligentemotionrecognitionfordriversusingmodellevelmultimodalfusion AT quanwen intelligentemotionrecognitionfordriversusingmodellevelmultimodalfusion AT bohang intelligentemotionrecognitionfordriversusingmodellevelmultimodalfusion |