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Research on driver's anger recognition method based on multimodal data fusion.

Wencai Sun¹, Yuwei Liu¹, Shiwu Li¹

¹Transportation College of Jilin University, Changchun, China.

Traffic Injury Prevention

|February 12, 2024

Summary

This summary is machine-generated.

This study introduces a multimodal driver anger recognition model using electrocardiographic (ECG) and driving behavior signals. The new model achieved 84.75% accuracy, significantly improving upon single-modal methods for driver emotion detection.

Keywords:

ECG signals Emotion recognition SVM driving behavior multimodal fusion

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Area of Science:

Human-Computer Interaction
Affective Computing
Transportation Safety

Background:

Single-modal driver anger recognition models suffer from low accuracy.
Existing methods often fail to capture the complexity of driver emotions.

Purpose of the Study:

To develop a multimodal driver anger recognition model.
To fuse electrocardiographic (ECG) and driving behavior signals for enhanced accuracy.

Main Methods:

Emotion-inducing experiments using a driving simulator.
Feature extraction from ECG and driving behavior signals.
Support Vector Machine (SVM) algorithm for binary classification.

Main Results:

Multimodal fusion significantly outperformed single-modal approaches.
The SVM-DS model achieved the highest accuracy of 84.75%.
Accuracy improvements of 9.10% (vs. ECG) and 4.15% (vs. behavior) were observed.

Conclusions:

The proposed multimodal model effectively identifies driver anger.
Provides theoretical and technical support for driver anger detection systems.