Research on Driver Model Based on Elastic Net Regression and ANFIS Method

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models. The simulation results show that compared with the real driver, the fuel consumption deviation of the ANFIS driver model is 3.5%, and the Jensen-Shannon similarity of pedal distribution is 0.034, which is significantly better than the other methods. The ANFIS driver model has a good tracking effect on different driving cycles and performs better than other models.