论文标题
关于分析轨迹预测基准中序列复杂性的简短说明
A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks
论文作者
论文摘要
序列复杂性的分析和量化是定义轨迹预测基准时经常遇到的一个开放问题。为了启用数据基础的更有信息的组装，提出了一种根据一组可区分的原型子序列来确定数据集表示的方法。该方法采用序列比对，然后采用学习矢量量化（LVQ）阶段。合成生成和现实世界数据集的第一个概念证明显示了该方法的生存能力。
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.
