Edge Computing Assisted Pothole Detection and Lane Identification Augmented by Federated Learning

Roads are transport infrastructure, and regular maintenance ensures safety and efficiency. Deteriorating asphalt can lead to potholes, increasing the risk of accidents. Therefore, this paper evaluates the deep learning architecture for detecting potholes and facilitating lane change assistance, involving federated learning and edge computing for road safety improvement. The pothole detection module uses CNNs trained on various road image datasets to identify potholes. Federated learning allows vehicles to train the model locally using their data and share updated model parameters with a central server, preserving privacy and reducing data transmission. Integration of TensorFlow and Keras ensures efficient model deployment and high accuracy in pothole detection. Once potholes are detected, it is activated by the lane change assistance system that aids accident prevention. Edge computing locally processes data, ensuring that the decisions and lane changes occur without delays in real time. The models are continuously improved because data from several vehicles contribute to a more robust system with federated learning. This approach improves pothole detection but also enhances lane change assistance, reducing accident risks. Federated learning is ensured to be privacy-preserving, and edge computing ensures fast processing. The experimental results demonstrate that the system surpasses the existing models in accuracy, safety, and real-time performance.