# Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques
## 6 Cluster Interpretation and Feasible Recommendations
### Driving Style Analysis  
Driving styles can be deduced based on cluster assignment by utilizing Figure 9, which shows the cluster distribution for models created by the RFE variables (left, Ci clusters, _Solution 1_ from Section 6.1) and PCA variables (right, Di clusters, _Solution 2_ from Section 6.2). Those figures show the number of windows belonging to each cluster for each driver traveling the same route. The number of windows determines the duration of the route; faster drivers will have fewer windows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline _Cluster_ & _MSDV Level_ & _Driving Pattern_ & _Recommendation_ \\ \hline _D1_ & _Low_ & _None_ & _None_ \\ (_Green_) & & & \\ \hline _D2_ & _Low_ & _High ERPM_ & _None_ \\ (_Cyan_) & & & \\ \hline _D3_ & _Medium_ & _High GP, PGP,_ & _Reduce GP, PGP_ \\ (_Blue_) & & & _Medium VS_ & _Reduce VS, BP_ \\ \hline _D4_ & _Medium_ & _High VS,_ & _Reduce VS, BP_ \\ (_Purple_) & & & _Medium-High BP_ & _Reduce SV, SP_ \\ \hline _D5_ & _Medium_ & _Medium SW_ & _Reduce SW_ \\ (_Orange_) & & & \\ \hline _D6_ & _High_ & _High SW and BP_ & _Reduce SW, BP_ \\ (_Red_) & & & \\ \hline _D7_ & _High_ & _High SW,_ & _Medium Everything_ & _Reduce SW, VS, BP_ \\ \hline \hline \end{tabular}
\end{table}
Table 8: Motion sickness level of each cluster, the characteristic that causes discomfort in passengers and the recommendations given to reduce it.  
As the model obtained in _Solution 2_ offers a more diverse cluster result with seven distinct clusters, we will use this clustering to determine driver style differences. Table 9 summarizes the figure interpretation, driving style determination, and possible recommendations.  
Many of these conclusions are applicable if we use the solution obtained by applying RFE (Figure 9a); however, since the models are different, some patterns are not completely represented and making direct links between the two models can be indicative but not decisive. Drivers that have the lowest amounts of C1 windows, also have low D1 and D2 cluster memberships. The only notable exception seems to be IM1051, which has 80% of C1 windows but only a small number of windows are in D1. We can safely say that both models have categorized the low MSDVxy samples of each driver in different yet similar ways. Concerning clusters related to high steering wheel usage of the PCA model (D5, D6, and D7) and RFE variables (C3, C4, C5), it seems that D5 is perfectly represented in the C3 cluster in driving style distribution. With C4 and C5, direct comparisons are not as useful since the error of a small number of windows could completely change our evaluation, but we can observe how all five drivers with no C4 and C5 clusters do not have D6 or D7 (except IM1086, which has a low D6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Drivers** & \
\begin{tabular}{c} **Distinctive Clustering** \\ **or Special Features** \\ \end{tabular} & **Possible Recommendations** \\ \hline IF1019 & Fastest driver & - \\ \hline IM1070, IM1072, IM1089 & Slowest drivers & - \\ \hline IM1072 & Best driver & - \\  & Highest number of D1 & - \\  & No high-risk clusters & \\ \hline IF1019, IM1060, IM1079, & High number of D2 &
\begin{tabular}{c} Smoother gear change and \\ ERPM \\ \end{tabular} \\ IM1084, IM1087 & High number of D3 & Smoother GP, PGP and ERPM \\ \hline IM1051, IM1062 & High number of D4 & Smoother VS and BP \\ \hline \hline \end{tabular}
\end{table}
Table 9: Driving style categorization, driver special features, and possible recommendations.  
Figure 9: Cluster assignments from the chosen model created after using the RFE feature selection (**a**), \(C_{i}\) clusters of Solution 1) or PCA (**b**), \(D_{i}\) clusters of Solution 2) on the driver-controlled variables. Each vertical bar represents one driver, starting with IF1017 and ending with IM1089; the bar represents how many points from each driver belongs to what cluster. This allows us to visualize the distribution of each driver and its comparison to others.