Federated learning system on autonomous vehicles for lane segmentation

• Published in: Scientific reports 2024-10, Vol.14 (1), p.25029-20, Article 25029
• Main Authors: Eid Kishawy, Mohab M., Abd El-Hafez, Mohamed T., Yousri, Retaj, Darweesh, M. Saeed
• Format: Article
• Language: English
• Subjects: 639/166; 639/166/987; Automation; Autonomous vehicles; Federated learning; Humanities and Social Sciences; Image segmentation; Lane segmentation; multidisciplinary; Real-time inference; Residual blocks; Science; Science (multidisciplinary); Segmentation; Training
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-71187-8

Autonomous Vehicles (AV) is one of the most evolving industries in the last decade. However, one of the bottlenecks of this evolution is providing data that contains different scenarios and scenes to improve the models without exposing the privacy and security of the edge vehicles. The authors of this research propose a secure and efficient novel solution for lane segmentation in AVs through the use of Federated Learning (FL). FedLane involves initial training of U-Net, ResUNet, and ResUNet++ models, followed by real-time inference in edge devices and the application of FL to update the server model using clients’ data. The study found that FL has enhanced the performance of lane segmentation significantly over baseline, enabling decentralized privacy-preserving collaborative optimization with increased dice coef from 0.9429 to 0.9794 for U-Net, from 0.9291 to 0.9854 for ResUNet and from 0.9079 to 0.9675 for ResUNet++. Additionally, the models show increased stability over the training iterations, highlighting the potential of FL to play a significant role in the future of automation in the AV industry.