Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
Journal article, 2025

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods.

Traffic safetyassessment

Adaptiveimportance sampling

Inverseprobability weighting

Optimal design

Computer simulationexperiments

Active learning

Author

Henrik Imberg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Xiaomi Yang

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Carol Ann Cook Flannagan

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Jonas Bärgman

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Technometrics

0040-1706 (ISSN) 1537-2723 (eISSN)

Vol. 67 1 46-57

Improved quantitative driver behavior models and safety assessment methods for ADAS and AD (QUADRIS)

VINNOVA (2020-05156), 2021-04-01 -- 2024-03-31.

Supporting the interaction of Humans and Automated vehicles: Preparing for the Environment of Tomorrow (Shape-IT)

European Commission (EC) (EC/H2020/860410), 2019-10-01 -- 2023-09-30.

Areas of Advance

Transport

Subject Categories (SSIF 2011)

Probability Theory and Statistics

DOI

10.1080/00401706.2024.2374554

More information

Latest update

4/4/2025 8