Semi-Automated Data Annotation in Multisensor Datasets for Autonomous Vehicle Testing

TL;DR

This paper introduces a semi-automated annotation pipeline for multisensor datasets in autonomous vehicle testing, combining AI and human input to efficiently generate high-quality annotations for large-scale driving data.

Contribution

It presents a novel human-in-the-loop annotation system that integrates AI, domain adaptation, and data anonymization to improve efficiency and consistency in multimodal dataset annotation.

Findings

Significant reduction in annotation time.

High-quality, consistent annotations across sensors.

Effective integration of AI and human expertise.

Abstract

This report presents the design and implementation of a semi-automated data annotation pipeline developed within the DARTS project, whose goal is to create a large-scale, multimodal dataset of driving scenarios recorded in Polish conditions. Manual annotation of such heterogeneous data is both costly and time-consuming. To address this challenge, the proposed solution adopts a human-in-the-loop approach that combines artificial intelligence with human expertise to reduce annotation cost and duration. The system automatically generates initial annotations, enables iterative model retraining, and incorporates data anonymization and domain adaptation techniques. At its core, the tool relies on 3D object detection algorithms to produce preliminary annotations. Overall, the developed tools and methodology result in substantial time savings while ensuring consistent, high-quality annotations across different sensor modalities. The solution directly supports the DARTS project by accelerating the preparation of large annotated dataset in the project's standardized format, strengthening the technological base for autonomous vehicle research in Poland.