## 3 Simulation and Verification  
### Verification of the Driving Style Recognition Model  
In this paper, five semi-trailers were used for the study. To reduce the influence of vehicle performance and driving sections on driving operation, the batch of vehicles had the same engine parameters and were driven on the same test section of the Guangzhou Expressway G80. All vehicles were driven by experienced drivers and the drivers were not informed in any way during the experiment to avoid the influence of the driver's psychological state on the experiment. On board diagnostics (OBD) was installed on the experimental vehicles from which the relevant data fields were extracted at the end of the journey on the experimental section and the data was uploaded via the CAN bus to complete the acquisition. Considering the instability of the sensors, some of the raw data were lost or abnormal, so the box plot method was used for data cleaning and interpolation was used to fill in the data. In addition, considering that continuous prolonged parking is not beneficial for driving style segmentation, it was removed together with the data cleaning process. A box-and-whisker plot [38] is a graphical representation of a dataset that displays, in a compact and standardized manner, information about the location, dispersion, degree of dispersion, and crosstalk of anomalous observations. Typically, a box plot as shown in Figure 9 consists of four parts: the median, the box, the tentacles extending from the box, and the outliers. The process of processing anomalous data using the box plot method is shown in Figure 10. Considering the continuity of the second data and their changing characteristics during the data acquisition process, it is highly reliable to use the linear interpolation method for data correction based on the elimination of outliers. Taking the vehicle driven by driver number 47 as an example, this paper uses the k-means method to initialize the markers of the original data. The definition of driving style varies from scholar to scholar and is usually classified into 2-4 categories. Considering the practical significance of driving style, the clustering results are divided into three categories in this paper. Figure 8: Diagram of the adaptive model prediction control simulation platform considering driving style and slope. The data with initial labels obtained from K-means were imported into the whale optimization algorithm for feature selection, and after several tests and parameter adjustments, the number of search agents and the number of iterations were set to 16 and 70, respectively. 97.34% of the features were selected accurately according to the fitness function used in this paper. In order to verify the relevance of the selected features, [PERSON]'s correlation coefficient method was used to determine the relevance of the features, and it was found that the correlation coefficient between throttle opening and cyclic fuel injection amount reached 0.97. Taking into account the correlation between the features, the feature of throttle opening was excluded. The correlation results for the remaining features are shown in Figure 7, indicating that the selected features are relatively independent and can be used to analyze the influence on driving style. To avoid overfitting problems caused by too many layers in the self-encoder, two Bi-LSTM layers with 32 cells are used as an encoder and decoder in the self-encoder. The selected features were also applied to the self-encoder as input. Based on several tests of the model, the models with 80 epochs and 32 batches were trained using TensorFlow, respectively, to improve the training efficiency of the model while maintaining accuracy. Finally, the weight matrix obtained through training is applied to the eigenvalues and eigenvectors of the spectral embedding to calculate the driving style recognition results.