Multi-parameter driver intention recognition based on neural network

Abstract

In this paper, the vehicle state parameters during driving are obtained through simulated driving experiments, the corresponding parameter change rules are analyzed, the characteristic parameters describing the intention are selected, and a sample library is established. The driver intention recognition model is built based on BP neural network, and the model is trained based on the data samples in the sample library to obtain the driver intention recognition model. The performance of the model was then analyzed, and the single working condition and compound working condition were verified in the model verification stage. From the experimental results, it can be seen that the intention model can accurately identify the driver's intention under a single operating condition. Under composite operating conditions, the vehicle's deviating behavior from the center line of the lane is similar to the lane changing behavior, so the model recognition results have certain errors, but the model can be accurately identify the driver's intention.