Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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Research and Analysis of Core Data in the Closed-loop of Autonomous Driving Data

Authors
Tielin Wang1, *
1School of AI and Advanced Computing, Xian Jiaotong-Liverpool University, Suzhou, 215123, China
*Corresponding author. Email: Tielin.Wang24@student.xjtlu.edu.cn
Corresponding Author
Tielin Wang
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_88How to use a DOI?
Keywords
Autonomous Driving; Data Closed-Loop; Data Cleaning; Automated Annotation; Federated Learning
Abstract

Since 2020, the global autonomous driving (AD) market has moved into the mass-market of L2 + Advanced Driver Assistance System (ADAS) penetration, it is believed that the adoption rate of the systems in China will reach more than 65 percent by 2025. A vehicle with AD capabilities produces 4–20 terabytes of multimodal data every day, which is the primary factor of optimization of the AD algorithms. But the large amount of data, data diversity, and real time processing capacities of AD data pose significant challenges to the conventional data analysis tools. In this paper, a systematic exploration of five fundamental data analysis technologies in the AD data closed-loop (including data collection - data cleaning - annotation - model training - simulation verification) is carried out, namely: multi-sensor data cleaning, automated annotation, training data selection, simulation test data analysis and privacy-preserving data collaboration. Each technology is discussed in the paper with its fundamental principles of operation, benefits, and their drawbacks as well as performance in open datasets (Waymo, NuScenes). The purpose is to define technical bottlenecks and suggest the way of development further and provide the researcher and engineering specialists with a comprehensive reference to the effective and safe AD data processing.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_88How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Tielin Wang
PY  - 2026
DA  - 2026/04/24
TI  - Research and Analysis of Core Data in the Closed-loop of Autonomous Driving Data
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 815
EP  - 820
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_88
DO  - 10.2991/978-94-6239-648-7_88
ID  - Wang2026
ER  -