The traditional PID controller is widely used to improve the stability of the system because of its simplicity and convenience. However, the disadvantage of the control method is that the control parameters are difficult to adjust in real-time, and it can not adapt to the needs of variable control targets in different states, affecting the controller’s practical application effect. Therefore, using the idea of hierarchical learning self-adjusting control, this paper optimizes the traditional PID controller based on BP neural network. The experimental results show that the controller can automatically adjust the control parameters, improve the control accuracy and achieve rapid response.

Aiming at the problem that there is a strong eardrum pressure in the passenger car during the closing process, two analysis and prediction methods, fast formula prediction and CFD simulation based on accurate models, are proposed. The regression model of ear pressure comfort was established by DOE method and multiple linear regression; The simulation software star-CCM+ is applied to simulate and analyze the dynamic characteristics of the flow field in the cockpit during the closing process by using the overlapping grid technology, and the pressure change curve near the ear is obtained. Finally, the CFD numerical simulation model is established by comparing and analyzing the regression prediction analysis results and the real vehicle test data. The results show that the effects of closing speed, effective opening area of pressure relief valve and air tightness of the whole vehicle on the pressure of passengers’ eardrums decrease in turn, and the prediction error of multiple linear regression equation is 17 %; The analysis error of the internal flow field dynamic characteristic model based on refined modeling is 8 %. This study provides a theoretical basis for solving the problem of rapid prediction of eardrum pressure and optimization of engineering structure.

To address the difficulty of environment perception in unmanned driving technology, an improved intelligent environment perception model based on YOLOv7 loss function is proposed. Simultaneously, the algorithm of deep sort target tracking is added for the sake of perceiving the surrounding environment through the camera to ensure the safety of the driver. And by modifying the loss term of the aspect ratio to the distinction between the predicted width and the target width, the fitting speed of the model can be effectively improved. Focal Loss is introduced to optimize the problem with the sample imbalance inside the target bounding box regression and reduce the weight proportion of numerous anchor boxes with less overlap between the predicted target bounding box and the true target bounding one to the overall model, such that the model centers more upon overlapping targets. The data applied to this article is BDD100K. The map of this algorithm reaches 60.3 %, which reaches 2.2 percentage points exceeding the baseline. Tests indicate that the raised algorithm attains certain advancements in the intelligent perception model of unmanned driving.