Multimodal fusion framework: comprehensive information utilization and performance optimization in driver style classification

The study proposes a multimodal fusion framework termed Vision-Motion Multimodal Classification (VMMC), with the objective of addressing the inherent complexities in the classification of driver styles. This framework amalgamates visual and motion data, leveraging the harmonious interplay between the visual modality feature extraction module and the motion feature extraction module, complemented by the integration of a cross-modal attention mechanism, to achieve precise classification of driver driving styles. Through meticulous experimental evaluation, the VMMC framework demonstrates substantial advantages across metrics such as precision, recall, and F1 score, thus validating the superiority of the VMMC framework. These research findings not only provide novel perspectives on the application of multimodal fusion in driver style classification but also offer invaluable insights for a deeper understanding of driving style patterns.