A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks
This addresses the open problem of sequence complexity analysis for researchers in trajectory prediction, but it appears incremental as it builds on existing methods like LVQ.
The paper tackled the problem of analyzing and quantifying sequence complexity in trajectory prediction benchmarks by proposing an approach that uses sequence alignment and learning vector quantization to represent datasets with prototypical sub-sequences, demonstrating viability on synthetic and real-world datasets.
The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks. In order to enable a more informative assembly of a data basis, an approach for determining a dataset representation in terms of a small set of distinguishable prototypical sub-sequences is proposed. The approach employs a sequence alignment followed by a learning vector quantization (LVQ) stage. A first proof of concept on synthetically generated and real-world datasets shows the viability of the approach.