In this paper, we present a novel approach for driving style classification based on time series data. Instead of automatically learning the embedding vector for temporal representation of the input data with Recurrent Neural Networks, we propose a combination of Hyperdimensional Computing (HDC) for data representation in high-dimensional vectors and much simpler feed-forward neural networks. This approach provides three key advantages: first, instead of having a “black box” of Recurrent Neural Networks learning the temporal representation of the data, our approach allows to encode this temporal structure in high-dimensional vectors in a human-comprehensible way using the algebraic operations of HDC while only relying on feed-forward neural networks for the classification task. Second, we show that this combination is able to achieve at least similar and even slightly superior classification accuracy compared to state-of-the-art Long Short-Term Memory (LSTM)-based networks while significantly reducing training time and the necessary amount of data for successful learning. Third, our HDC-based data representation as well as the feed-forward neural network, allow implementation in the substrate of Spiking Neural Networks (SNNs). SNNs show promise to be orders of magnitude more energy-efficient than their rate-based counterparts while maintaining comparable prediction accuracy when being deployed on dedicated neuromorphic computing hardware, which could be an energy-efficient addition in future intelligent vehicles with tight restrictions regarding on-board computing and energy resources. We present a thorough analysis of our approach on a publicly available data set including a comparison with state-of-the-art reference models.
Multivariate Time Series Analysis for Driving Style Classification using Neural Networks and Hyperdimensional Computing
11.07.2021
1301470 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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