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The analysis and quantification of sequence complexity is an open problemfrequently encountered when defining trajectory prediction benchmarks. In orderto enable a more informative assembly of a data basis, an approach fordetermining a dataset representation in terms of a small set of distinguishableprototypical sub-sequences is proposed. The approach employs a sequencealignment followed by a learning vector quantization (LVQ) stage. A first proofof concept on synthetically generated and real-world datasets shows theviability of the approach.

A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks