# 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
Among the car-following principal component clustering centers, the longitudinal factor is the biggest difference, and the corresponding results of each type are 0.071, \(-\)2.627, and 3.828. The longitudinal factor of type 3 is the largest, and that of type 2 is the smallest. It is known that the driver corresponding to type 3 shows higher speed and acceleration in longitudinal movement. Moreover, there are many rapid acceleration and deceleration behaviors, and the acceleration control is unstable, indicating that its driving style is radical. The corresponding results of longitudinal displacement factors are 1.136, \(-\)0.942, and \(-\)0.662. This factor of type 1 is the largest, indicating that its car-following driving state is the most stable. Judging that the driving-style type is moderate, then type 2 is conservative. However, the longitudinal correlation factor fails to draw the above rules, and it is impossible to judge the driving style through it. Table 4 is the statistical value of data characteristics before the dimensionality reduction of the car-following clustering results. Compared with the clustering results of PCA, we can see the differences between different driving styles more intuitively and obviously. Figure 8: Silhouette coefficient graph of clustering results: (**a**) car-following state, (**b**) lane-changing state. Figure 7: SSE graph of clustering results: (**a**) car-following state, (**b**) lane-changing state. Among the lane-changing principal components, the clustering center of the longitudinal speed change factor has the greatest difference. The corresponding results of each type are \(2.800\), \(-0.148\), and \(-3.572\). The factor of type 1 is the largest, followed by type 2 and type 3. It shows that drivers in type 1 tend to have higher acceleration and jerk in the longitudinal movement of vehicles, and the acceleration control is unstable. It is judged that the driving style of type 1 is radical, type 2 is moderate, and type 3 is conservative. The lane-changing factor cannot summarize the obvious type differences. The corresponding results of each type of lateral factor are \(0.058\), \(-0.005\), and \(-0.070\). Type 1 has the largest lateral factor. It is judged that the lateral movement is more radical in lateral movement and has worse instability. However, type 2 is slightly larger than type 3, so it is more conservative to judge type 3, and the conclusion is the same as that obtained by the longitudinal movement of vehicles. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Result** & **Conservative** & **Moderate** & **Radical** \\  & **(Type 2)** & **(Type 1)** & **(Type 3)** \\ \hline Maximum speed & 40.314 & 50.019 & 60.104 \\ Mean speed & 30.319 & 35.645 & 43.907 \\ Standard deviation of speed & 5.064 & 7.126 & 7.388 \\ Maximum absolute value of acceleration & 2.936 & 3.416 & 3.650 \\ Mean value of acceleration absolute value & 0.643 & 0.708 & 0.947 \\ Standard deviation of absolute acceleration value & 0.694 & 0.772 & 0.887 \\ Maximum acceleration & 2.763 & 3.259 & 3.533 \\ Average acceleration & 0.686 & 0.761 & 0.989 \\ Maximum deceleration & \(-2.636\) & \(-3.181\) & \(-3.412\) \\ Average deceleration & \(-0.644\) & \(-0.707\) & \(-0.919\) \\ Car-following time & 270.14 & 524.76 & 318.247 \\ Distance traveled during car-following time. & 223.095 & 503.518 & 382.406 \\ Mean space headway & 18.799 & 21.87 & 25.115 \\ Standard deviation of space headway & 3.564 & 4.66 & 5.187 \\ Mean time headway & 2.349 & 2.247 & 2.079 \\ Standard deviation of time headway & 0.443 & 0.42 & 0.370 \\ Average value of positive jerk & 2.882 & 2.977 & 3.946 \\ Average value of negative jerk & \(-2.984\) & \(-3.054\) & \(-4.058\) \\ Maximum positive jerk & 11.429 & 13.041 & 14.939 \\ Maximum negative jerk & \(-12\) & \(-13.583\) & \(-15.727\) \\ Rapid acceleration times & 0.573 & 2.767 & 5.630 \\ Rapid deceleration times & 0.412 & 2.344 & 5.110 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Clustering results of car-following feature parameters.