## 3 Experimental Set-Up
### Data Acquisition and Preprocessing
#### 3.4.3 The Output and Input Variables of the Optimized SVR Model  
(1) The output variables  
In this paper, the output variable of the optimized SVR model is a driving decision, may be free driving, car following, or lane changing. We assign the represented values and the output threshold ranges to all of the driving decisions, as seen in Table 2. For example, if an output value of DDM falls within the threshold range (\(-1.5\), \(0.5\)), it represents that the driving decision is free driving.  
(2) The input variables  
Solving practical problems often need to collect a lot of indexes to reflect more information about the research object. If the correlation between these indexes is high, then the information reflected from them will have a certain overlap, which will increase the complexity of processing information. To solve this problem, Principal Component Analysis (PCA) is proposed to analyze data indexes and obtain the needed input variables [26].  
PCA is a statistical analysis method. It can transform multiple correlated indexes into a few of uncorrelated indexes. The comprehensive indexes, called the principal components, will keep the original indexes information as much as possible. If there is a \(p\)-dimensional random vector \(f=(f_{1},\,f_{2}\ldots,\,f_{p})^{\prime}\), using PCA, the \(p\) reference indexes can be transformed into a set of uncorrelated principal indexes \(x_{1},x_{2},\ldots,x_{p}\) as their principal components, as seen in (14).  
\[\left\{\begin{array}{l}x_{1}=a_{11}f_{1}+a_{12}f_{2}+\ldots+a_{1p}f_{p}\\ x_{2}=a_{21}f_{1}+a_{22}f_{2}+\ldots+a_{2p}f_{p}\\........................................................\\ x_{p}=a_{p1}f_{1}+a_{p2}f_{2}+\ldots+a_{pp}f_{p}\end{array}\right. \tag{14}\]  
Then, \(m(m<p)\) principal components need to be selected from above \(p\) principal components to adequately reflect the information represented by \(f_{p}\). The number of principal components \(m\) depends on the cumulative contribution rate of the variance \(G(m)\).  
\[G(m)=\sum_{i=1}^{m}\lambda_{i}/\sum_{k=1}^{p}\lambda_{k} \tag{15}\]  
where \(\lambda_{i}\) is the eigenvalue of \(x_{i}\).  
Usually, when \(G(m)>85\%\), these \(m\) principal components can adequately reflect the information of the original \(p\) reference indexes.  
Then, we use PCA to make the correlation analysis of 11 reference indexes through 200 sets of samples. The analysis process of PCA is shown in Figure 6. The calculated results of PCA for each principal component are shown in Figure 7. According to the cumulative contribution rate of the variance of each principal component, the first five principal components \(\textbf{X}=[x_{1},\,x_{2}\,,\,\ldots,\,x_{5}]\) are selected as the input variables of the optimized SVR model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Driving Decision** & **Symbol** & **Represented Value** & **Threshold Range of the Output Value** \\ \hline Free driving & \(y_{1}\) & \(-1\) & (\(-1.5\), \(0.5\)) \\ Car following & \(y_{2}\) & \(0\) & (\(-0.5\), \(0.5\)) \\ Lane Changing & \(y_{3}\) & \(1\) & (\(0.5\), \(1.5\)) \\ \hline \end{tabular}
\end{table}
Table 2: Driving Decision-Making Behaviors.