Kreutz, Thomas ; Esbel, Ousama ; Mühlhäuser, Max ; Sanchez Guinea, Alejandro (2022):
Unsupervised Driving Event Discovery Based on Vehicle CAN-data.
In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 4169-4174,
IEEE, 25th International Conference on Intelligent Transportation Systems, Macau, Peoples Republik of China, 08.-12.10.2022, ISBN 978-1-6654-6880-0,
DOI: 10.1109/ITSC55140.2022.9922158,
[Conference or Workshop Item]

Abstract

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.

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