Abstract
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.
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Notes
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\(\text {Interior points:}(f(x+h)-f(x-h))/2h\), for evenly spaced \((h=1)\).
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\(\text {End points:} (f(x+h)-f(x))/h \text { and }(f(x)-f(x-h))/h\), for evenly spaced \((h=1)\).
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\(\text {ADF}(W_t),\text { if p-value}>0.01 \text { signals is non-linear, else linear.}\).
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Acknowledgement
This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, Grant Agreement n\(^{\underline{\text {o}}}\) 955393. Moreover, thanks to Manufacture Française des Pneumatiques Michelin for support and car dataset provision.
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Lebese, T., Mattrand, C., Clair, D., Bourinet, JM., Deheeger, F. (2024). Unsupervised Representation Learning for Smart Transportation. In: Miliou, I., Piatkowski, N., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14642. Springer, Cham. https://doi.org/10.1007/978-3-031-58553-1_2
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