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End of preview. Expand in Data Studio
RAF-Ready Argoverse 2
This repository provides the processed Argoverse 2 dataset used in the paper:
Retrieval-Augmented Dataset Assembly for Fair Clustering
The data are prepared for evaluating RAF, a retrieval-augmented dataset assembly framework for downstream fair clustering. RAF studies a data-level solution to fair clustering: instead of only imposing fairness constraints on a fixed dataset, it retrieves and selects real external samples to reduce minority under-representation before clustering.
This release contains the RAF-ready processed version of Argoverse 2, including the query dataset, external source files, source metadata, trajectory embeddings, and sensitive-group labels required to run RAF.
This repository does not redistribute the full original Argoverse 2 dataset. It only provides the processed files used for RAF experiments. Users should also comply with the license and terms of the original Argoverse 2 dataset.
Dataset Description
Background
Fair clustering aims to produce clustering results that are both useful and fair with respect to sensitive groups. However, when the original dataset severely under-represents a minority group in some semantic regions, fair clustering algorithms are limited by the available data. They can rebalance assignments, but they cannot create missing real samples.
RAF addresses this limitation from the data side. Given an initial query dataset and a collection of external data sources, RAF selectively retrieves candidate samples under a budget constraint. It then evaluates whether each candidate improves majority-minority distributional alignment in the embedding space. The accepted samples are used to augment the initial dataset before downstream clustering.
This processed Argoverse 2 release is designed to support this setting in the autonomous driving domain, where trajectory patterns of different actor groups may be unevenly distributed across the embedding space.
Source Dataset
The original dataset is based on Argoverse 2, a large-scale autonomous driving dataset for perception and forecasting research.
Original dataset:
- Argoverse 2: https://www.argoverse.org/av2.html
- Argoverse User Guide: https://argoverse.github.io/user-guide/
- Official API repository: https://github.com/argoverse/av2-api
In RAF, we use the Argoverse 2 motion forecasting data to construct trajectory-level embeddings and source partitions for retrieval-augmented fair clustering.
Processed Dataset
This repository contains the processed RAF-ready Argoverse 2 files:
- Query dataset used as the initial dataset
D_q - External source files used as candidate data sources
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