# Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

arXiv: https://arxiv.org/abs/2103.03786 · HTML: https://mlsanity.com/paper/2103.03786/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019)
- [Performance Optimization for Federated Person Re-identification via Benchmark Analysis](https://mlsanity.com/paper/2008.11560/) (2020)
- [Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data](https://mlsanity.com/paper/1912.12204/) (2019)
- [Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks](https://mlsanity.com/paper/2002.08440/) (2020)
- [TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data](https://mlsanity.com/paper/1909.07867/) (2019)
- [Learning Centric Power Allocation for Edge Intelligence](https://mlsanity.com/paper/2007.11399/) (2020)
- [Distributed Multi-Target Tracking for Autonomous Vehicle Fleets](https://mlsanity.com/paper/2004.05965/) (2020)

## Built on by (most cited first, 24 of 24)

- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [Deep Federated Learning for Autonomous Driving](https://mlsanity.com/paper/2110.05754/) (2021)
- [Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds](https://mlsanity.com/paper/2208.00116/) (2022)
- [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://mlsanity.com/paper/2208.10590/) (2022)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [MACP: Efficient Model Adaptation for Cooperative Perception](https://mlsanity.com/paper/2310.16870/) (2023)
- [Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model](https://mlsanity.com/paper/2104.10378/) (2021)
- [VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection](https://mlsanity.com/paper/2212.07060/) (2022)
- [A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles](https://mlsanity.com/paper/2203.05192/) (2022)
- [Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2306.16169/) (2023)
- [CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusion](https://mlsanity.com/paper/2310.06008/) (2023)
- [Selective Communication for Cooperative Perception in End-to-End Autonomous Driving](https://mlsanity.com/paper/2305.17181/) (2023)
- [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://mlsanity.com/paper/2504.04631/) (2025)
- [Accelerating Federated Edge Learning via Topology Optimization](https://mlsanity.com/paper/2204.00489/) (2022)
- [Edge-Aided Sensor Data Sharing in Vehicular Communication Networks](https://mlsanity.com/paper/2206.08882/) (2022)
- [Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling](https://mlsanity.com/paper/2107.11588/) (2021)
- [Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology](https://mlsanity.com/paper/2207.09657/) (2022)
- [Graded-Q Reinforcement Learning with Information-Enhanced State Encoder for Hierarchical Collaborative Multi-Vehicle Pursuit](https://mlsanity.com/paper/2210.13470/) (2022)
- [A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL](https://mlsanity.com/paper/2401.00809/) (2024)
- [Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication](https://mlsanity.com/paper/2505.03528/) (2025)
- [Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization](https://mlsanity.com/paper/2108.13669/) (2021)
- [StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests](https://mlsanity.com/paper/2407.18148/) (2024)
- [SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perception](https://mlsanity.com/paper/2607.23910/) (2026)

## 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.