# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 5 Discussions  
The comparison of the ACO-BP driving style recognition model with the BP driving style recognition model alone yields relatively limited results. Therefore, the artificial  
Figure 11: Driving style recognition speed of the BP model.  
Figure 12: Driving style recognition speed of the ACO-BP model.  
neural network (ANN) driving style recognition model and the gradient boosting machine (GBM) driving style recognition model were introduced to compare accuracy and real-time performance. The simulation results are shown in Figure 13. It can be observed that, compared to the ACO-BP model's three error points, the ANN model has eight error points, and the GBM model has 18 error points. The ACO-BP model exhibits significantly fewer errors in sample recognition in comparison to the ANN and GBM models. This indicates that the ACO-BP model possesses a greater advantage in terms of recognition accuracy, enabling better differentiation of driving styles. The chances of avoiding aggressive driving styles are enhanced, which ensures better road safety in autonomous driving.  
The driving style recognition speeds of the artificial neural network (ANN) driving style recognition model and gradient boosting machine (GBM) driving style recognition model are shown in Figures 14 and 15, respectively. The slowest recognition speed for the ANN model is 9 ms, while the fastest recognition speed is 5 ms. On the other hand, the fastest recognition speed for the GBM model is 10 ms, whereas the slowest recognition speed is 7 ms. Compared to the ANN model, the GBM model exhibits relatively slower recognition speed.  
In this paper, the recognition accuracy and speed ranges of the four driving style recognition models mentioned are shown in Table 9. First, in terms of recognition accuracy, the ACO-BP model has the highest recognition accuracy, significantly higher than the other three driving style recognition models. This ensures the ability of autonomous vehicles to accurately distinguish risky driving behaviors and timely avoid driving risks. Secondly, the recognition speed range of the ACO-BP model is close to that of the BP model, and it is the lowest among the four models. However, by observing the simulation results of the recognition speed of the four models, it is evident that the recognition speed range distribution of the ACO-BP model is the most uniform, with most samples having a recognition speed of 5 ms or 6 ms and only a small number of samples having a recognition speed of 7 ms. This ensures the real-time recognition of driving styles for autonomous vehicles, allowing them to make the fastest recognition while driving on actual roads.  
Figure 13: Prediction results of the ANN and GBM driving style recognition models.  
Figure 14: Driving style recognition speed of the ANN model.  
Figure 15: Driving style recognition speed of the GBM model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Recognition Accuracy** & **Recognition Speed Range (ms)** \\ \hline ACO-BP & 96.7\% & [5,7] \\ BP & 90\% & [5,8] \\ ANN & 91.11\% & [5,9] \\ GBM & 80\% & [7,10] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Recognition results of driving styles for each model.