# A Method of Intelligent Driving-Style Recognition Using Natural Driving Data
## 4 Result
### Data Modeling  
#### 4.3.1 Clustering Model of Driving Style Based on K-Means Algorithm  
After obtaining the results of dimensionality reduction, this paper chooses the K-means algorithm to establish a driving-style classification model and classify driving styles. On the basis of the existing research, the cluster number of the driving style is comprehensively judged by SSE and silhouette coefficient. The value range of k was set to to 1-9, clustered in turn, and then the SSE and silhouette coefficient of the clustering result corresponding to each k value were calculated. The results obtained are shown in Figures 7 and 8. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number** & **Characteristic Parameter** & \multicolumn{6}{c}{**Principal Component**} \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline X1 & Maximum speed & 0.236 & \(-\)0.375 & 0.116 & \(-\)0.026 & \(-\)0.059 & \(-\)0.049 & 0.051 \\ X2 & Mean speed & 0.193 & \(-\)0.418 & 0.110 & \(-\)0.094 & \(-\)0.045 & \(-\)0.059 & \(-\)0.033 \\ X3 & Standard deviation of speed & 0.173 & \(-\)0.074 & 0.089 & 0.301 & \(-\)0.078 & 0.026 & 0.253 \\ X4 & Maximum absolute value of acceleration & 0.241 & 0.110 & \(-\)0.243 & 0.269 & \(-\)0.243 & \(-\)0.078 & 0.002 \\ X5 & Mean value of acceleration absolute value & 0.334 & 0.051 & \(-\)0.060 & 0.016 & \(-\)0.016 & 0.020 & 0.082 \\ X6 & Standard deviation of absolute acceleration value & 0.270 & 0.110 & \(-\)0.220 & 0.242 & \(-\)0.297 & \(-\)0.029 & 0.045 \\ X7 & Maximum acceleration & 0.203 & 0.075 & \(-\)0.106 & 0.432 & 0.051 & \(-\)0.262 & \(-\)0.152 \\ X8 & Average acceleration & 0.277 & 0.028 & 0.052 & 0.308 & 0.215 & \(-\)0.148 & \(-\)0.070 \\ X9 & Maximum deceleration & \(-\)0.184 & \(-\)0.148 & 0.322 & 0.265 & 0.337 & \(-\)0.143 & \(-\)0.023 \\ X10 & Average deceleration & \(-\)0.241 & \(-\)0.127 & 0.203 & 0.311 & 0.239 & \(-\)0.146 & \(-\)0.141 \\ X23 & Average value of positive jerk & 0.314 & 0.091 & 0.001 & \(-\)0.122 & 0.216 & 0.066 & 0.016 \\ X24 & Average value of negative jerk & \(-\)0.311 & \(-\)0.093 & 0.020 & 0.122 & \(-\)0.257 & \(-\)0.071 & \(-\)0.007 \\ X25 & Maximum positive jerk & 0.211 & 0.143 & \(-\)0.074 & \(-\)0.181 & 0.412 & 0.045 & \(-\)0.163 \\ X26 & Maximum negative jerk & \(-\)0.219 & \(-\)0.090 & 0.043 & 0.158 & \(-\)0.446 & \(-\)0.003 & \(-\)0.008 \\ X29 & Maximum lateral speed & 0.167 & 0.121 & 0.478 & \(-\)0.019 & \(-\)0.149 & 0.139 & 0.073 \\ X30 & Mean lateral speed & \(-\)0.010 & \(-\)0.004 & 0.045 & 0.235 & 0.104 & 0.729 & 0.183 \\ X31 & Standard deviation of lateral speed & 0.182 & 0.097 & 0.459 & \(-\)0.013 & \(-\)0.154 & 0.122 & 0.038 \\ X36 & Lane-changing time & \(-\)0.162 & \(-\)0.087 & \(-\)0.350 & 0.244 & 0.148 & 0.383 & \(-\)0.052 \\ X37 & Distance traveled during lane-changing time & 0.083 & \(-\)0.440 & \(-\)0.169 & 0.073 & 0.048 & 0.221 & \(-\)0.088 \\ X38 & Maximum heading angle & \(-\)0.082 & 0.448 & 0.177 & 0.107 & \(-\)0.025 & 0.145 & 0.077 \\ X39 & Interchangeable gap of target lane & 0.111 & \(-\)0.247 & 0.223 & 0.178 & 0.025 & 0.057 & 0.017 \\ Mean value of space headway before & & 0.011 & \(-\)0.201 & \(-\)0.116 & \(-\)0.155 & 0.080 & \(-\)0.129 & 0.743 \\ X41 & lane-changing & & & & & & & \\ X42 & Mean value of time headway before lane-changing & \(-\)0.150 & 0.215 & 0.021 & 0.112 & \(-\)0.018 & 0.349 & 0.497 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Principal component kernel load matrix of lane-changing. Considering SSE and silhouette coefficient comprehensively, in order to more clearly reflect the differences between various driving styles and obtain driving-style types with large differences in characteristics, this paper selects k = 3 as the clustering category number of K-means clustering. According to the K-means algorithm, cluster analysis is made on car-following data and lane-changing data, and vehicles in two states are divided into three types. The results are shown in Figure 9,b.