FedLANE: a federated U-Net architecture for lane detection

Santhiya Santhiya (Karunya Institute of Technology and Sciences)
Immanuel Johnraja Jebadurai (Karunya Institute of Technology and Sciences)
Getzi Jeba Leelipushpam Paulraj (Karunya Institute of Technology and Sciences)
Polisetti Pavan Venkata Vamsi (Karunya Institute of Technology and Sciences)
Madireddy Aravind Reddy (Karunya Institute of Technology and Sciences)
Praveen Poulraju (Karunya Institute of Technology and Sciences)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 32, No 3: December 2023

Lane detection is a crucial module for today’s autonomous driving cars. Detecting road lanes is a challenging task as it varies in color, texture, boundaries and markings. Traditional lane detection techniques detect the lane by applying a model trained with centralized data. As roads vary in urban and rural areas, a more localized and decentralized training technique is desired for accurate and personalized lane detection. Federated learning has recently proved to be a promising technology that trains and prunes the model using local data. Applying federated learning-based lane detection improves the accuracy of detection and also ensures the security and privacy of autonomous cars. This paper proposes FedLANE, a federated learning-based lane detection technique. U-Net, U-Net long short-term memory (LSTM) and AU-Net architectures were explored using a federated learning approach. Experimental analysis using TuSimple and CuLane dataset shows that the FedLANE based lane detection performs similar to that of the traditional deep learning lane detection models.