# Driver Clustering Based on Individual Curve Path Selection Preference
## 3 Results
### Driver Clustering
The kinematic primitives (e.g., mean velocity, maximum longitudinal and lateral acceleration) are compared. Such quantities are also proposed by the literature [14; 15; 40]. The results are given in Table 3. Considering the average speed and the average lateral acceleration, no significant change can be observed between the clusters. It is noted that the speed is also influenced by the traffic, which may be significantly different between the test drives. On the other hand, the maximum velocity, the highest deceleration and acceleration, as well as the average longitudinal absolute acceleration, are significantly different from each other. That means, the dynamic drivers are also more dynamic considering the general vehicle kinematics, further proving that the aforementioned clustering can indeed separate different driving styles. From practical utilization perspectives, the dynamic driving group must be analyzed to ensure that, in the event of reproducing this behavior, the system would not lead to unsafe scenarios. The standard lane width on the test route was 3.8 m, while the vehicle width is approximately 1.8 m. Then, the maximum lane offset of approximately \(\pm 1\) m of the dynamic group would mean that the edge of the vehicle exactly touches the edge of the lane. This is an acceptable behavior if there are no obstacles (e.g., oncoming traffic) in the other lane. Also, from the viewpoint of the dynamic driver, the behavior is  
Figure 4: Map of the road segment, which is used for the simulations. Turquise color indicates a left curve, yellow indicates a right curve section of the road. This is a recorded route from a real road section, main road 31, Hungary. understandable: he wants to use the entire space available. From the ADAS point of view, such a lane offset is usually not acceptable. There are two ways of tackling this: first, a limitation can be applied to the calculated lane offset, which can ensure that the ego vehicle would not get too close to other objects. Secondly, for up to SAE level 2 systems [41], the lane following function works in cooperation with the driver; therefore, a higher lane offset threshold can be defined in curve cutting scenarios, before the system starts to steer against the driver. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Cluster 1** & **Cluster 3** \\ \hline Curve cutting extent—left curve & low & high \\ \hline Curve cutting extent—right curve & moderate & high \\ \hline Curve entry outer driving & moderate & moderate \\ \hline Driving style & cautious & dynamic \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Driving cluster features and driving styles associated with them. Figure 5: Simulation of an experimental curve combination with clustered parameters. The plot shows the lane offset, which is planned by using parameters of the individual drivers in the same cluster as well as the cluster centroids. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\overline{v}_{x}\) & \(\textbf{max}(v_{x})\) & \(\textbf{min}(a_{x})\) & \(\textbf{max}(a_{x})\) & \(\overline{|a_{x}|}\) & \(\overline{|a_{y}|}\) \\ \hline Cluster 1 (cautious) & 23.62 & 30.35 & \(-\)4.09 & 3.29 & 0.29 & 0.32 \\ Cluster 3 (dynamic) & 23.50 & **33.06** & \(-\)**6.28** & **5.92** & **0.75** & 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the clusters based on primitive kinematic features of the cluster elements. Bold quantities highlight that cluster 3 has a more dynamic behavior from kinematic point of view.