## 4 Empirical Results
\begin{split} PQC=((\sum\limits_{i=1}Q_{i}W_{i})/QC\ Total\ Score)\times 100\\ Q_{i}\ :Quick\ Check\ Unit\ Score,\ W_{i}\ :Weight\ of\ Quick\ Check\ Q_{i}\end{split}  \tag{3}\]  
In the case of the TC calculation formula, the TC sum was calculated by multiplying the score of each TC item by the importance weighting factor that considered the selection of security experts, relevance of hacking cases, etc., and then adding them. Then, the score was divided by the total TC score to calculate the percentage score of TC (PTC), as shown in Equation (4). \[ \begin{split} PTC=((\sum\limits_{i=1}T_{i}W_{i})/TC\ Total\ Score)\times 100\\ T_{i}\ :Total\ Check\ Unit\ Score,\ W_{i}\ :Weight\ of\ Total\ Check\ T_{i}\end{split}  \tag{4}\]  
In order to verify the validity of QC, the PQC value of QC and the PTC values of TC were derived, and the validity was verified by comparing the difference between the two values. If we look into it in more detail, the pseudocode expression that checks \(|PTC-PQC|<10\), which is set as the validity value, is as follows in Figure 4. The \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Items** & **Previous Studies** & **Proposed Model** \\ \hline \multirow{4}{*}{Compliance} & \multirow{2}{*}{Standard} & Individual analysis of each standard & Analysis of integrated standards including UNR No.155, ISO/BAE 21434, etc. \\  & & Almost none & Analysis of laws in major countries including the US, UK, and Germany \\ \hline \multirow{4}{*}{Research Method} & \multirow{2}{*}{
\begin{tabular}{c} Fragmentary Studies \\ for focusing on \\ technological security \\ trends \\ \end{tabular} } & Integrated Study that combines administrative, technical, and physical security items \\ \hline \multirow{4}{*}{Check items and their relative  importance, etc.} & \multirow{4}{*}{Few previous  studies} & The first check items derived through an integrated analysis of compliance, security threats, etc. \\  & & Identification of relative importance \\ \cline{1-1}  & & Ranking by applying AHP \\ \cline{1-1}  & & Introduction of top-ranking partial check for quick identification and reduced input resources \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with previous studies and the proposed model. function that calculates the \(PQC\) value, which is the final obtained score of \(QC\), is defined as \(\text{PQC}\) ( ), and the function that calculates the \(PTC\) value, which is the final obtained score of \(TC\), is defined as \(\text{PTC}\)( ) to calculate the values. After that, the validity is verified by checking the difference between the two values through the Check_VQC( ) function. The number of evaluation data to be collected, which are the check items of \(TC\) and \(QC\), are 77 and 25, respectively. Looking at the tier companies that performed TC and QC, in the case of tier company A, the final QC score is 76.98 points when calculated with Equation (1), and the final TC score is 73.70 points when calculated with Equation (4). As \(|\text{PTC-PQC}|<10\) was set as the validity value above, the score difference between these two inspections is 3.6 points, so QC can be said to be valid. For tier companies B and C, the values were calculated using the above formulas and the validity values were checked, and they were within the threshold values. These values are shown in Figure 4.  
That is, the results obtained using functions such as \(\text{PQC}\)( ) and \(\text{PTC}\)( ) are as shown in Figure 5, and the validity of QC was verified as the score difference between the checks was smaller than the set threshold of 10. Therefore, if there are many tier companies and there is a lack of time and resources to conduct the security-level check of autonomous vehicles, the security level can be identified more quickly if QC, which is the top priority items in the relative importance derived from this study, is performed first. In addition, the security-level check time and costs of autonomous vehicles can be reduced. Figure 4: QC validation expressions.