Multisource heterogeneous data fusion: multimodal modeling approach for intelligent safety driving assistance system

Unsafe driver behavior is a significant cause of traffic accidents, and accurately identifying and warning of dangerous driving behaviors is crucial for improving road traffic safety. Single-modal data alone is insufficient to comprehensively describe complex driving behaviors. Multimodal data fusion technologies, by integrating heterogeneous information sources such as vision, speech, and motion, provide robust support for driving behavior analysis. This paper comprehensively reviews the current status, key technologies, application cases, challenges, and future development trends of safety driving behavior warning models based on multimodal fusion. It introduces typical architectures and algorithms of multimodal deep learning models, such as early/late/mixed fusion strategies, attention mechanisms, and temporal modeling, and analyzes application scenarios such as intelligent driving assistance systems and driving simulation training. Finally, it highlights challenges such as model performance, data quality and computational requirements, and discusses future directions. The development of multimodal safety driving behavior analysis technology will make important contributions to the construction of intelligent transportation systems.