Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion - Xiaofeng Feng; Chenhui Liu; Ying Chen (IEEE Access, 2024)
Road rage can easily cause serious road traffic accidents. In order to detect emotional changes of drivers in a straightforward and accurate way, and enable measures to be taken to avoid accidents, this paper presents a road rage emotion detection method called DMG-FCC for drivers of motor vehicles based on the fusion of MFCC and GFCC speech features in the frequency domain and their first-order differential features ( $Δ $ MFCC, $Δ $ GFCC). These features are first analysed, and each feature selected for fusion is determined preliminarily based on experience. To improve the detection efficiency, principal component analysis (PCA) is used to reduce the dimensionality of the fused features. Six principal components are selected as detection features based on the proportion of the variance values of the principal components of each dimension. The sparrow search algorithm is used to optimise the support vector machine classifier, and an SSA-SVM road rage emotion detection model is established and trained to recognise road rage emotions in drivers. The driver emotion dataset is established, and enhanced by changing the speech rate, pitch, and volume, through expansion, on the basis of the original 200 pieces of data, a total of 1400 pieces of data were got for each type of dataset. Experiments show that feature fusion gives better anti-noise performance than other single feature detection methods. Compared with other detection models, our method yields good detection performance, with a high detection accuracy of 95.2% on original dataset, and 96.8% on enhanced dataset, which can meet the real driving requirements.