# A Method of Intelligent Driving-Style Recognition Using Natural Driving Data
## 3 Methodology
### Data Processing and Analysis  
There are many states when the vehicle is driving. In order to comprehensively and reasonably study driving styles, it is necessary to divide the vehicle trajectories in the dataset into different driving states. Then, the vehicle groups are extracted to establish datasets for different driving states. Finally, we conduct a detailed analysis of the factors that affect drivers' driving styles for different driving states, thus laying the foundation for the design of subsequent driving-style recognition methods.  
In addition, in the process of data collection, data anomalies, noise, and other factors will inevitably affect the quality of datasets. Therefore, it is necessary to smooth the extracted data. In this paper, the Savitzky-Golan filter algorithm is selected for smoothing [60, 61]. Based on the polynomial least squares method [62], the data points are fitted and smoothed by setting the window length. By continuously moving the position of the window, the entire data sequence is smoothed to reduce the influence of noise and fluctuations.  
After data processing, the car-following dataset and lane-changing dataset are obtained. The characteristic parameters representing the driving style in car-following and lane-changing states are extracted to form a multi-dimensional characteristic parameter matrix. It is necessary to show the differences between various driving styles as much as possible.  
The car-following state is not only influenced by its own vehicle characteristic parameters but also by the preceding vehicle. In this paper, 28 evaluation parameters are extracted from car-following data, forming a characteristic parameter matrix. Compared with the car-following state, in addition to considering the relationship between the own vehicle and the preceding vehicle, the lane-changing state also needs to consider the relationship with the target lane of the lane-changing. A total of 29 evaluation parameters are extracted from lane-changing data to form a characteristic parameter matrix. The composition of car-following characteristic parameters and lane-changing characteristic parameters is shown in Table 1.  
In order to fully reflect the driving style, it is inevitable to increase the number of characteristic parameters. However, this will increase the data dimension, easily ignore  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Common Parameters** & **Car-following Characteristic Parameters** & **Lane-changing Characteristic Parameters** \\ \hline Speed & The proportion of time above the average speed & Lateral speed, acceleration, jerk \\ Acceleration & Car-following time & Lane-changing time and driving distance \\ Jerk & Distance traveled during car-following time & Maximum heading angle \\ Space headway & Rapid acceleration times & Interchangeable gap of target lane \\ Time headway & Rapid deceleration times & Time headway of lane-changing target gap \\ etc. & etc. & etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-dimensional feature parameters.  
the potential connections within the dataset, and lead to the complexity of the recognition model structure. It will not only increase the amount of calculation and storage space but also lead to overfitting risks [40] and parameter redundancy, making it difficult to interpret driving-style classification results. Therefore, before the classification and recognition of driving style, correlation analysis and principal component analysis (PCA) are used to optimize the driving-style characteristic parameters. PCA refers to transforming the original data into a set of linearly independent representations through linear transformation when there is a certain correlation among the variables in the data. Retaining all the principal components that make the cumulative contribution rate exceed 80% can improve the accuracy of the model and reduce the amount of calculation, and provide good sample data for the next driving-style clustering.