Unsupervised Representation Learning for Smart Transportation

In the automotive industry, sensors collect data that contain valuable driving information. The collected datasets are in multivariate time series (MTS) format, which are noisy, non-stationary, lengthy, and unlabeled, making them difficult to analyze and model. To understand the driving behavior at specific times of operation, we employ an unsupervised representation learning method. We present Temporal Neighborhood Coding for Maneuvering (TNC4maneuvering), which aims to understand maneuverability in smart transportation data via a use-case of bivariate accelerations from three operation days out of 2.5 years of driving. Our method proves capable of extracting meaningful maneuver states as representations. We evaluate them in various downstream tasks, including time-series classification, clustering, and multi-linear regression. Moreover, we propose methods for pruning the sizes of representations along with a window-size optimizing algorithm. Our results show that TNC4maneuvering has the capacity to generalize over longer temporal dependencies, although scalability and speedup present challenges.