Vehicle-mounted Early Warning System Based on Edge Intelligence

With the steady development of the social economy, the number of motor vehicles has increased rapidly. The number of traffic accidents continues to rise, and fatigue driving is one of the key factors leading to frequent traffic accidents. The Internet of Things (IoT) technology based on cloud computing is receiving more and more attention, and the requirement of device for edge of IoT also has been increased. The amount of data generated by devices has exploded, which has brought a higher demand for data transmission bandwidth. Therefore, this paper proposes an edge intelligent vehicle-mounted early warning system based on Gradient Boosting Decision Tree (GBDT), YOLOv4, and Deep Simple Online And Realtime Tracking (Deep SORT) algorithms. The system does not need to transmit data to the cloud center for processing, and realizes dual monitoring inside and outside the vehicle locally, effectively alleviating the pressure on transmission bandwidth. The result performed that the edge system can detect driver fatigue status and object categories outside the vehicle in real time, which has a positive effect on reducing traffic accidents and developing intelligent transportation.