In today's data-driven world, extracting meaningful insights from complex datasets is crucial for progress and innovation. This challenge takes on new dimensions with multivariate time series, where multiple signals intertwine to describe a phenomenon. The thesis tackles this challenge in the context of Toyota Motor Europe, focusing on segmenting Controller Area Network (CAN) data, a cornerstone of modern vehicles. Motivated by the need to understand driver behavior better, this thesis presents a novel pipeline for unsupervised segmentation of multivariate CAN time series. Leveraging the potential of data-driven approaches, the system automatically identifies significant events within the data, eliminating the need for manual labeling or prior knowledge.