Back-to-back (B2B) test has been introduced as a pivotal method for ensuring equivalence between model-level and implementation-level behaviour during the validation process of Automotive Software Systems (ASSs). Conventionally, the analysis of B2B execution results depends on the application of expert knowledge and the utilisation of predetermined thresholds for the identification of failures. This approach, however, is limited in its ability to address the complexities inherent in modern systems, particularly in the presence of non-linear dynamic multivariate behaviour under noisy conditions. To address these limitations, in this study, an intelligent analysis approach is proposed for assisting the test engineers during the analysis process of the B2B test execution results. The approach is capable of automatically detecting and clustering the faults in an efficient manner considering the noisy conditions. To this end, a CNN-LSTM-based denoising autoencoder (DAE) architecture has been developed to accurately detect the faults in the test recordings based on fault-free dataset. Furthermore, an adopting density-based clustering method, i.e., DBSCAN, has been proposed to group the detected faults based on representative features extracted from DAE. The evaluation results demonstrate the superiority of the proposed approach in comparison to state-of-the-art methods in terms of performance and computational cost with F1-score 96.15%, DBI 0.159 and testing time 5.2 ms. Additionally, the experimental findings demonstrate that the proposed approach satisfy the criteria for robustness and generalisation in the presence of noise across diverse driving scenarios with MSE of 0.00312 at 10% noise. Consequently, the proposed approach has the potential to reduce the time and effort associated with traditional analysis while improving the safety and reliability of complex dynamic vehicle systems.