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Cited by 76Autonomous Vehicle Technology and SafetyRobotics and Sensor-Based LocalizationPrivacy-Preserving Technologies in Data

Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

Open access
Zhang, Zijian, Wang, Shuai, Hong, Yuncong, Zhou, Liangkai, Hao, Qi·2021·2021 IEEE International Conference on Robotics and Automation (ICRA)
DOI 10.1109/icra48506.2021.9561612
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Abstract

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores; (2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters; (3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the CARLA simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.

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  • Study [190] employed a three-stage map fusion (density-based spatial clustering, score-based averaging, and intersection-over-union-based pruning) to enable accurate, privacy-preserving trajectory predictions.

    “Meanwhile, [190] employed a map fusion in three stage (density-based spatial clustering, scorebased averaging, and intersection-over-union-based pruning) to enable accurate, privacy-preserving trajectory predictions.”

    From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [190]

    The Introduction (p. 2) explicitly lists the three fusion stages: DBSCAN clustering, score-based weighted averaging, and IoU-based box pruning. But the paper describes these stages as producing a global object map, not trajectory predictions; the cited passages also do not establish the claimed privacy-preserving outcome. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 24

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Authors
Zhang, Z., Wang, S., Hong, Y., Zhou, L., & Hao, Q.
Affiliations
  • Harbin Institute of Technology (CN)
  • Southern University of Science and Technology (CN)
ORCID
Venue
2021 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2021-05-30
Pages
953-959
ISSN
1050-4729 (ISSN-L)
DOI
10.1109/icra48506.2021.9561612
OpenAlex
W3134982056
Cited by
76
References
3
Citations by year
2026: 4 · 2025: 14 · 2024: 18 · 2023: 12 · 2022: 18 · 2021: 10
FWCI
14.36
Type
conference paper
Language
en
Open access
Licence
Locations
  • Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
federated learning, connected vehicles, representation learning, knowledge distillation, missing labels, online learning, CARLA simulator, autonomous driving

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