# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 4 Results
### ACO-BP-Based Driving Style Model Identification and Result Analysis  
The US-101 freeway vehicle data in the NGSIM dataset was selected and screened to identify 301 free-lane-changing vehicles that met the requirements. Table 6 presents the specific sample data. After the sample data were analyzed by PCA downscaling and K-means clustering analysis, the inputs of the driving style model were the top 4 principal component scores \([Y_{1},Y_{2},Y_{3},Y_{4}]\) with a cumulative variance contribution rate greater than 85%, and the clustering result dataset of the K-means clustering analysis was the output of the model. In order to ensure that the neural network can fully learn the features and patterns of the data, 70% of the data is classified as the training data of the neural network, and the remaining 30% is classified as the test data of the neural network. To ensure the validity of the test data and enhance the accuracy of the model's recognition, the selection of data samples was determined based on the outcomes of the K-means clustering analysis. Specifically, the sample set is composed of 25% radical data, 49% ordinary data, and 26% conservative data. MATLAB is used to simulate and analyze the ACO-BP driving style recognition model, and the errors and accuracies of the simulated model are shown in Table 7. The ACO-BP model achieves a recognition accuracy of 96.7%, showing a significant improvement of 6.7% compared to the BP model's recognition accuracy of 90%. The errors of the ACO-BP model are smaller than those of the BP model, and the accuracy of recognition surpasses that of the BP model. The simulation results comparing the recognition errors of the BP model and the ACO-BP model are shown in Figure 8. It is evident that the optimized model has fewer error points, resulting in smoother graphs. The error values of all points tend to be closer to 0. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Test Track**} & **Sample Dimension** & **Sample Size** & **Training Data** & **Test Data** & **Selection Method** \\ \hline US-101 & 6 & 301 & 211 & 90 & even-handed \\ \hline \end{tabular}
\end{table} 
Table 6: Sample data. Figure 7: Evolutionary convergence curve. The ACO-BP driving style recognition model has a clear advantage in terms of the overall error of the two models, with higher recognition accuracy. However, according to the relevant research, due to the more uncertain and dangerous driving behavior of aggressive-style drivers, they have significantly higher accident rates than the other two driving styles. Therefore, at the same time, the ACO-BP model is required to recognize the effect of each driving style and cannot have a large error in order to prevent the over-fitting problem. To further investigate the model's effectiveness in recognizing driving styles in a more detailed manner, individual simulations are conducted to examine the impacts of identifying the three driving styles. The simulation results are shown in Figures 9 and 10, which clearly show that the ACO-BP model has only 1, 0, and 2 samples producing recognition errors for each of the three driving styles of aggressive, ordinary, and conservative, respectively, while the BP model has 4, 2, and 3 samples producing recognition errors for each of the three driving styles, respectively. This indicates that, more than just the overall error, the ACO-BP model has better recognition accuracy than the BP model. When targeting different types of driving styles, the ACO-BP model is able to perform good recognition, and the recognition accuracy does not vary greatly due to the change in driving styles. The specific recognition accuracy results for the 3 driving styles are shown in Table 8, which shows that for different driving styles, the recognition accuracy of the ACO-BP model is higher than that of the BP model. For the three driving styles of aggressive, normal, and conservative, the recognition accuracies are much improved, respectively: 13.7%, 4.4%, and 4.3%.