# A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks

arXiv: https://arxiv.org/abs/2004.04677 · HTML: https://mlsanity.com/paper/2004.04677/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction](https://mlsanity.com/paper/2107.00422/) (2021)

## Abstract (arXiv)

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.