Recent advancements in vehicle driving data analysis have attracted significant attention as a key research area aimed at optimizing driving behavior and enhancing vehicle performance. This study aims to collect operational data from drivers, including steering angle, longitudinal acceleration, brake usage, and wheel speed, and to cluster driving segments based on their characteristics using deep learning techniques. To achieve this, we employed a methodology that integrates the LSTM-Autoencoder model, which captures essential features of time-series data through compression and reconstruction, with an Attention mechanism. The driving segments were clustered and visually represented with distinct colors for clarity. Experimental results from the proposed approach demonstrated an over 96% accuracy in aligning input and output values, facilitating the clustering of driving segments based on their distinctive features. These findings provide valuable insights for improving driver behavior and optimizing vehicle assistance systems, potentially contributing to significant advancements in this field of research.