# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
###### Abstract  
To enhance the lane-changing safety of autonomous vehicles, it is crucial to accurately identify the driving styles of human drivers in scenarios involving the coexistence of autonomous and human-driven vehicles, aiming to avoid encountering vehicles exhibiting hazardous driving patterns. In this study, based on the real traffic flow data from the Next Generation Simulation (NGSIM) dataset in the United States, 301 lane-changing vehicles that meet the criteria are selected. Six evaluation parameters are chosen, and principal component analysis (PCA) is employed for dimensionality reduction in the data. The K-means algorithm is then utilized to cluster the driving styles, classifying them into three categories. Finally, ant colony optimization (ACO) of a backpropagation (BP) neural network model was constructed, utilizing the dimensionality reduction results as inputs and the clustering results as outputs for the purpose of driving style recognition. Simulation experiments are conducted using MATLAB Version 9.10 (R2021a) for comparative analysis. The results indicate that the constructed ACO-BP model achieved an overall recognition accuracy of 96.7%, significantly higher than the recognition accuracies of the BP, artificial neural network (ANN), and gradient boosting machine (GBM) models. The ACO-BP model also exhibited the fastest recognition speed among the four models. Moreover, the ACO-BP model shows varied improvements in recognition accuracy for each of the three driving styles, with an increase of 13.7%, 4.4%, and 4.3%, respectively, compared to the BP model. The simulation results validate the high accuracy, real-time capability, and classification effectiveness of this model in driving style recognition, providing new insights for this field. traffic safety; autonomous vehicles; driving style; principal component analysis; K-means clustering; ant colony; optimize BP neural network +
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urban road traffic efficiency [6]. In the human-vehicle-road traffic system, drivers, as the main participants, have a significant impact on road traffic safety [7]. Different driving styles have varying effects on vehicle lane-changing safety [8]. For instance, drivers with aggressive driving styles have higher demands for speed and time benefits, resulting in reduced safety levels and a higher accident rate. Therefore, researching driver style recognition technology is of great significance as it can enhance the safety of lane changing for autonomous vehicles to a certain extent. The main objective of current research is to improve the accuracy and real-time capability of driver style recognition. At present, one widely applied technique in various fields, such as architecture and mathematical analysis, is the ACO-BP hybrid model, known for its accurate prediction performance and fast prediction speed [9; 10; 11]. Therefore, this study aims to investigate the practical application of the ACO-BP hybrid model in the direction of autonomous driving style recognition. In this paper, real vehicle driving data from the NGSIM dataset is utilized, and vehicle historical trajectory data that meet the requirements are selected to avoid the risk of overfitting caused by small-sample data. By using principal component analysis for dimensionality reduction and employing K-means clustering analysis to categorize driving styles, a driving style recognition model based on ant colony optimization of a BP neural network is established. This model enables the identification and prediction of driving styles, thus enabling the timely avoidance of hazardous driving styles during lane-changing maneuvers. The remaining organization of this paper is as follows: Section 2 provides an overview of the research status of automatic driving style recognition. Section 3 presents the methodology and data, including a description of the ACO-BP model, the extraction and processing of vehicle data, feature extraction, and driving style clustering.