I aimed to uncover and analyze braking patterns in driving behavior using machine learning and Controller Area Network-BUS (CAN-BUS) data. My primary goal was to understand how drivers employ the brake pedal across different scenarios, including highways, rural areas, and urban settings. I followed a structured approach, starting with data preprocessing and feature selection, leading to the development of a Variational AutoEncoder (VAE)-based deep learning framework. I carefully selected input signals like speed, steering angle, and accelerations for their consistency across various vehicle models. Streamlining the model's complexity and enhancing its interpretability were key considerations. During data preprocessing, I addressed challenges in resampling signals to a common 5 Hz frequency, applying noise-cleaning techniques such as interpolation and filtering.