# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 2 Literature Review  
In the continuous advancement of autonomous driving technology, many scholars have come to consider that lane-changing accidents account for a considerable proportion of traffic accidents. Aggressive driving styles often involve frequent lane changes, smaller following distances, and more drastic acceleration changes, which highlights the importance of improving the accuracy of lane-changing recognition in autonomous driving to reduce safety hazards and mitigate such risky driving styles. Therefore, in the current research on the safety of autonomous driving lane changing, much attention has been focused on the field of automatic driving style recognition technology. One of the most widely applied technologies is traditional machine learning algorithms. Traditional machine learning algorithms mainly include support vector machines (SVMs), decision trees, and random forests. [PERSON] et al. compared the recognition effects of models using SVMs, adaptive boosting (AdaBoost), and random forest algorithms by extracting data from internal sensors in the engine through the On-Board Diagnostics II (OBD-II) protocol, achieving good recognition accuracy [12]. [PERSON] et al. proposed a driving style recognition model based on the Classification and Regression Tree (CART) decision-making method by clustering indicators such as following distance, time gap, and accelerator pedal opening, and their experimental results demonstrated its good recognition accuracy [13]. [PERSON] et al. collected natural driving trajectory data in freeway diverging areas using unmanned aerial vehicles and applied K-means++ to cluster the driving styles in lane-changing sections. They achieved an accuracy of 93% using a random forest model for driving style identification and prediction [14]. With the advancement of onboard sensor technology, vehicles are capable of collecting a large amount of data for deep learning training. Simultaneously, due to improve ments in computational power, algorithms, and other factors, deep learning has gained popularity in the field of automated driving style recognition [15]. [PERSON] et al. proposed a deep neural network architecture using unsupervised feature extraction, feature selection algorithms, and automated machine learning (AutoML) techniques. They validated the model using real road driving data in random environments, achieving a driving style recognition accuracy of 95% [16]. [PERSON] et al. developed an adaptive regularization convolutional neural network (CNN) to avoid overfitting when performing driving style recognition on smartphones [17]. [PERSON] et al. introduced a driving style recognition system with a combination of a fully convolutional network (FCN) and a squeeze-and-excitation (SE) block. They used psychophysical and behavioral data for driving style recognition and validation [18]. [PERSON] et al. proposed a comprehensive driving style recognition model based on driving cycle identification. They constructed a driving style cycle and recognition model using an artificial neural network (ANN), enabling more comprehensive and accurate long-term driving style recognition [19]. Nowadays, with the increase in vehicle ownership and the complexity of urban road networks, higher demands are being placed on autonomous driving technology. In response, hybrid models combining traditional machine learning algorithms with deep learning have emerged. [PERSON] et al. utilized the Next Generation Simulation (NGSIM) traffic flow dataset and proposed the Adaptive Multivariate Continuous Gaussian Mixture Hidden Markov Model (AMCGM-HMM) for dynamic driving intention recognition of surrounding vehicles, thereby enhancing vehicle driving safety [20]. [PERSON] et al. addressed incomplete traffic data and proposed a Convolutional Neural Network -- Long Short-Term Memory (CNN-LSTM)-based driving style recognition model, which improved recognition accuracy and generalization capabilities [21]. Additionally, [PERSON] et al.