The use of multivariate time series generation in industrial settings such as the automotive industry continues to increase. The complexity of data analysis requirements in such industries has led to an urgent need to develop effective methods for extracting structural information from data based on the clustering of system behavior time series. Because there are complex interactions between vehicle data variables, the time series clustering of single variables can lead to insufficient results. To the best of our knowledge, only univariate dynamic time warping (DTW) approaches have thus far been applied in an automotive context. To close this research gap, this paper presents a review of generic approaches in multivariate dynamic time warping (MDTW) to determine the most promising approaches for use in the automotive domain. Four approaches are found to be particularly useful for tasks such as the objective assessment of subjective driving perceptions.
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