# Data selection method for assessment of autonomous vehicles

arXiv: https://arxiv.org/abs/2407.12065 · HTML: https://mlsanity.com/paper/2407.12065/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Description of Corner Cases in Automated Driving: Goals and Challenges](https://mlsanity.com/paper/2109.09607/) (2021)
- [Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs](https://mlsanity.com/paper/2011.06165/) (2020)
- [Diverse Complexity Measures for Dataset Curation in Self-driving](https://mlsanity.com/paper/2101.06554/) (2021)
- [Scenario Extraction from a Large Real-World Dataset for the Assessment of Automated Vehicles](https://mlsanity.com/paper/2306.07815/) (2023)
- [FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images](https://mlsanity.com/paper/2309.03799/) (2023)
- [SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles](https://mlsanity.com/paper/2311.11580/) (2023)
- [Multiple data sources and domain generalization learning method for road surface defect classification](https://mlsanity.com/paper/2407.10197/) (2024)

## Built on by (most cited first, 2 of 2)

- [Improving classification of road surface conditions via road area extraction and contrastive learning](https://mlsanity.com/paper/2407.14418/) (2024)
- [FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systems](https://mlsanity.com/paper/2608.28672/) (2026)

## Abstract (arXiv)

As the popularity of autonomous vehicles has grown, many standards and regulators, such as ISO, NHTSA, and Euro NCAP, require safety validation to ensure a sufficient level of safety before deploying them in the real world. Manufacturers gather a large amount of public road data for this purpose. However, the majority of these validation activities are done manually by humans. Furthermore, the data used to validate each driving feature may differ. As a result, it is essential to have an efficient data selection method that can be used flexibly and dynamically for verification and validation while also accelerating the validation process. In this paper, we present a data selection method that is practical, flexible, and efficient for assessment of autonomous vehicles. Our idea is to optimize the similarity between the metadata distribution of the selected data and a predefined metadata distribution that is expected for validation. Our experiments on the large dataset BDD100K show that our method can perform data selection tasks efficiently. These results demonstrate that our methods are highly reliable and can be used to select appropriate data for the validation of various safety functions.