Multimodal Fusion Intelligent Driving Fatigue Monitoring and Warning in Police Traffic Scenarios

The study presents a multimodal fusion intelligent driving fatigue monitoring and warning system tailored for police traffic scenarios. This system integrates diverse data modalities, including facial expressions, body posture, heart rate, and speech, to achieve comprehensive and precise fatigue detection. Leveraging edge-cloud collaborative computing and infrared image recognition technology, the system operates efficiently across various environmental conditions, enhancing its reliability and stability. The system's adaptability is further demonstrated through its compatibility with different levels of autonomous driving (L1 to L5), offering customized solutions that range from assisted to fully autonomous driving. Specially designed for police operations, the system provides features such as fleet management, equipment integration, and real-time connectivity to command centers, which collectively improve the safety and effectiveness of law enforcement tasks. Compared to traditional single-modality systems, this multimodal approach significantly enhances detection accuracy, stability, and functional diversity, positioning it as a competitive solution for widespread application in both general and specialized driving contexts.