mlsanityData to 7 Oct 2026

Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

arXiv 2103.03786 ↗First posted 5 Mar 2021cs.LG cs.AI cs.MAcited by 24

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Abstract (arXiv)

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 Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 5 Mar 2021.

Builds on

14 of 27 references.

References · 14

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  3. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 366
  4. 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
  5. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
  6. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  7. 2019Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensorscited by 110
  8. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 77
  9. 2020Performance Optimization for Federated Person Re-identification via Benchmark Analysiscited by 50
  10. 2019Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 24
  11. 2020Cooperative LIDAR Object Detection via Feature Sharing in Deep Networkscited by 19
  12. 2019TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Datacited by 8
2 more
  1. 2020Learning Centric Power Allocation for Edge Intelligencecited by 3
  2. 2020Distributed Multi-Target Tracking for Autonomous Vehicle Fleetscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 8; 2023: 5; 2024: 3; 2025: 2; 2026: 1.

24 papers.

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  1. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  2. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  3. 2022Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Cloudscited by 29
  4. 2022A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperationcited by 21
  5. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  6. 2023MACP: Efficient Model Adaptation for Cooperative Perceptioncited by 14
  7. 2024Federated Distillation: A Surveycited by 14
  8. 2021Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Modelcited by 9
  9. 2022VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detectioncited by 8
  10. 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
  11. 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
  12. 2023CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusioncited by 6
12 more
  1. 2023Selective Communication for Cooperative Perception in End-to-End Autonomous Drivingcited by 5
  2. 2025Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspectivecited by 5
  3. 2022Accelerating Federated Edge Learning via Topology Optimizationcited by 4
  4. 2022Edge-Aided Sensor Data Sharing in Vehicular Communication Networkscited by 3
  5. 2021Accelerating Federated Edge Learning via Optimized Probabilistic Device Schedulingcited by 2
  6. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  7. 2022Graded-Q Reinforcement Learning with Information-Enhanced State Encoder for Hierarchical Collaborative Multi-Vehicle Pursuitcited by 1
  8. 2024A review on different techniques used to combat the non-IID and heterogeneous nature of data in FLcited by 1
  9. 2025Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communicationcited by 1
  10. 2021Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimizationcited by 0
  11. 2024StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requestscited by 0
  12. 2026SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perceptioncited by 0