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Federated learning system on autonomous vehicles for lane segmentation.

Mohab M Eid Kishawy^1,2, Mohamed T Abd El-Hafez², Retaj Yousri³

¹Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, L8S 4K1, Canada.

Scientific Reports

|October 23, 2024

Summary

Federated Learning (FL) enhances lane segmentation for autonomous vehicles (AVs) by enabling privacy-preserving data collaboration. This approach significantly improves model performance and stability for AV development.

Keywords:

Autonomous vehicles Federated learning Image segmentation Lane segmentation Real-time inference Residual blocks

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Area of Science:

Computer Science
Artificial Intelligence
Robotics

Background:

Autonomous vehicles (AVs) require extensive data for model training, posing privacy and security challenges.
Current data collection methods for AVs can compromise sensitive information from edge devices.
Developing robust lane segmentation models is crucial for safe AV operation.

Purpose of the Study:

To propose a novel Federated Learning (FL) solution, named FedLane, for secure and efficient lane segmentation in AVs.
To enhance AV model performance without centralizing sensitive data from edge vehicles.
To demonstrate the effectiveness of FL in privacy-preserving collaborative optimization for AVs.

Main Methods:

Initial training of U-Net, ResUNet, and ResUNet++ models for lane segmentation.
Real-time inference on edge devices within AVs.
Application of FL to update a central server model using decentralized data from clients.
Evaluation of model performance using the Dice coefficient metric.

Main Results:

Federated Learning significantly improved lane segmentation performance across all tested models.
Dice coefficients increased from baseline: U-Net (0.9429 to 0.9794), ResUNet (0.9291 to 0.9854), and ResUNet++ (0.9079 to 0.9675).
Models demonstrated increased stability throughout training iterations, indicating robust learning.

Conclusions:

Federated Learning offers a secure and efficient method for collaborative model optimization in AVs.
FL enhances lane segmentation accuracy and stability while preserving data privacy.
FL is a promising technology for the future of automation in the AV industry.