## 6 Conclusions  
This article presents a novel deep-learning-based intelligent clustering framework designed to facilitate the real-time validation of ASSs through the enhancement of the HIL testing process. The primary objective of the proposed approach is to improve the process of clustering and analyzing failed TCs. This allows the corresponding failure classes of failed TCs to be identified efficiently. To this end, the integration of a DL-based architecture with a clustering approach has been proposed as a means of addressing the challenge of poor clustering performance. In particular, a convolutional auto-encoder model has been constructed with the objective of extracting comprehensive representative features from the logs of failed TCs. Furthermore, the k-means algorithm has been employed for the purpose of clustering the extracted features, with the objective of associating each cluster with a specific category.  
The proposed framework has been evaluated using a HIL-based real-time virtual test drive platform comprising a high-fidelity entire vehicle model. The findings demonstrate the possibility of incorporating the proposed framework into the HIL testing process, thus allowing the generated report to be automatically analyzed without the need for domain expertise or code access. In accordance with the evaluation metrics, i.e., DBI and accuracy, the proposed clustering approach has demonstrated a noteworthy accomplishment in comparison to the traditional approach, with a score of 0.5184 and 94.3%, respectively. Furthermore, the proposed 1 dCNN-AE architecture for feature extraction has demonstrated superior performance in terms of MSE compared to other AE variants, including standard AE and LSTM-AE. In comparison to the traditional clustering model, the proposed clustering approach has achieved an improvement of 34.85% by leveraging the advantages of AE in  
Figure 10: k-means clustering of the extracted features.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Evacuation Metrics** & \begin{tabular}{c} **Standard AE +** \\ **k-Means** \\ \end{tabular} & \begin{tabular}{c} **LSTM-AE + k-** \\ **Means** \\ \end{tabular} & \
\begin{tabular}{c} **Standalone k-** \\ **Means** \\ \end{tabular} &
\begin{tabular}{c} **1 dCNN-AE +** \\ **k-Means** \\ \end{tabular} \\ \hline DBI Score & 1.0797 & 1.0690 & 1.2034 & 0.5184 \\ Accuracy & 62.26\% & 66.03\% & 59.49\% & 94.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clustering performance of the proposed approach compared to other AE variants.  
the extraction of comprehensive and meaningful features from the dataset. This approach constitutes a notable contribution to the field of real-time validation of automotive systems, reducing the cost of manual analysis in terms of time and effort.  
As future research, the proposed framework can be expanded to identify the root causes of the detected failure. Furthermore, the potential for applying the proposed framework to other safety-critical systems in diverse domains, such as railway and aviation, can be investigated.  
Conceptualization, M.A.; methodology, M.A.; software, M.A.; validation, M.A.; formal analysis, M.A.; investigation, M.A.; resources, M.A., C.K. and A.R.; data curation, M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A., C.K. and A.R.; visualization, M.A.; supervision, M.A., C.K. and A.R.; project administration, M.A., C.K. and A.R.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.  
We acknowledge support by the Open-Access Publishing Fund of Clausthal University of Technology.  
Not applicable.  
Data Availability Statement: Data available on request due to restrictions.  
The authors declare no conflict of interest.