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Driver Emotion Recognition Using Multimodal Signals by Combining Conformer and Autoformer.

Weiguang Wang1, Jian Lian1, Chuanjie Xu1

  • 1School of Intelligence Engineering, Shandong Management University 250357, Jinan, P. R. China.

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Summary

This study developed a multimodal system for driver emotion recognition, integrating facial expressions, ElectroCardioGram (ECG), and ElectroEncephaloGram (EEG) signals for enhanced accuracy in driver monitoring.

Keywords:
Multimodal emotion recognitionautoformerconformercross-attentiondeep learningmultimodal fusion

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Driver emotion recognition is crucial for vehicle safety and advanced driver-assistance systems.
  • Existing methods often rely on single modalities, limiting accuracy and reliability.
  • Integrating diverse data sources can provide a more comprehensive understanding of a driver's emotional state.

Purpose of the Study:

  • To develop a multimodal system for accurate driver emotion recognition.
  • To integrate facial expressions, ECG, and EEG signals for robust emotion detection.
  • To enhance driver monitoring systems through improved emotional state identification.

Main Methods:

  • A Conformer model was used to analyze facial images for visual emotion cues.
  • Two Autoformer models processed ElectroCardioGram (ECG) and ElectroEncephaloGram (EEG) signals.
  • A cross-attention mechanism fused embeddings from all three modalities, followed by classification.

Main Results:

  • The fusion of visual, physiological (ECG), and neurological (EEG) data significantly improved emotion detection accuracy and reliability.
  • The multimodal approach demonstrated superior performance compared to single-modality methods.
  • The system effectively identified driver emotional states during the driving process.

Conclusions:

  • Multimodal integration offers a promising approach for advanced driver emotion recognition.
  • The proposed system enhances insights into emotional processes relevant to driver safety.
  • This work provides a foundation for future advancements in affective computing and intelligent transportation systems.