Driving real-time risk assessment and early warning method considering individual heterogeneity of driver

The invention discloses a driving real-time risk assessment and early warning method considering individual heterogeneity of a driver, and the method comprises the steps: building a corresponding scene for the driving behaviors, such as average speed, acceleration standard deviation and steering wheel angle change rate, based on the road condition, vehicle model and vehicle condition related to driving, and the physiological and psychological conditions of a driver, and weather conditions; the method comprises the following steps: acquiring sample data, classifying the acquired sample data into corresponding scenes, constructing a three-layer artificial neural network model for each scene, finally judging the driving scene of a driver according to weather conditions and road conditions, and selecting a previously constructed neural network of the corresponding scene to evaluate the driving risk of the driver to obtain a corresponding early warning method. According to the method, the factors which have the gr