  The data collected from a vehicle's Controller Area Network (CAN) can quickly
exceed human analysis or annotation capabilities when considering fleets of
vehicles, which stresses the importance of unsupervised machine learning
methods. This work presents a simultaneous clustering and segmentation approach
for vehicle CAN-data that identifies common driving events in an unsupervised
manner. The approach builds on self-supervised learning (SSL) for multivariate
time series to distinguish different driving events in the learned latent
space. We evaluate our approach with a dataset of real Tesla Model 3 vehicle
CAN-data and a two-hour driving session that we annotated with different
driving events. With our approach, we evaluate the applicability of recent time
series-related contrastive and generative SSL techniques to learn
representations that distinguish driving events. Compared to state-of-the-art
(SOTA) generative SSL methods for driving event discovery, we find that
contrastive learning approaches reach similar performance.
