Identification and Assessment of Electric Bus Driver's Driving Behavior Based on Computer Vision and Intelligent Algorithms

How to accurately identify and evaluate the driver's driving behavior is very important for the safety of passengers, and it is difficult for current technology to accurately identify it. Therefore, this article combines computer vision and intelligent algorithm technology to develop a model to improve the recognition and evaluation of the driving behavior of electric bus drivers. It captures the driver's visual information in the car by installing a camera, and uses deep learning algorithms to recognize and analyze the driver's movements, posture, and facial expressions. At the same time, it collects vehicle dynamic data and environmental data to provide contextual information, and establishes driving behavior models and evaluation indicators to evaluate and classify the driver's driving behavior in this way. The experimental results show that the recognition time of the model in this article is maintained at 0.4-1s, and the model can recognize and evaluate the driver's behavior in a timely and accurate manner.