Detection and Research on Unsafe Driving of Taxi Drivers

Abstract

In response to the unsafe driving of taxi drivers during the epidemic, the design and implementation of the driver's face registration and detection of whether the driver wears a mask, detection of the driver's fatigue physiological signals and multiple integration are designed. Target detection algorithm based on MobileNetV2 to realize mask detection. Combine MTCNN and Face-Net organically to realize driver's face login. The cerebellar neural network model is used to fuse the 12 fatigue monitoring signals EMG, EEG, etc. extracted by the simulated driving experiment platform to obtain a multi-integrated fatigue monitoring control model. After the fatigue driving multiple physiological indicators of the simulated driving platform, the technical model is studied and verified. It is concluded that the multiple fusion fatigue monitoring control model has higher accuracy than the traditional single signal monitoring.