Research Article
Federated Learning for Drowsiness Detection in Connected Vehicles
@INPROCEEDINGS{10.1007/978-3-031-49379-9_9, author={William Lindskog and Valentin Spannagl and Christian Prehofer}, title={Federated Learning for Drowsiness Detection in Connected Vehicles}, proceedings={Intelligent Transport Systems. 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings}, proceedings_a={INTSYS}, year={2023}, month={12}, keywords={Federated Learning Driver Drowsiness Connected Vehicles}, doi={10.1007/978-3-031-49379-9_9} }- William Lindskog
Valentin Spannagl
Christian Prehofer
Year: 2023
Federated Learning for Drowsiness Detection in Connected Vehicles
INTSYS
Springer
DOI: 10.1007/978-3-031-49379-9_9
Abstract
Ensuring driver readiness poses challenges, yet driver monitoring systems can assist in determining the driver’s state. By observing visual cues, such systems recognize various behaviors and associate them with specific conditions. For instance, yawning or eye blinking can indicate driver drowsiness. Consequently, an abundance of distributed data is generated for driver monitoring. Employing machine learning techniques, such as driver drowsiness detection, presents a potential solution. However, transmitting the data to a central machine for model training is impractical due to the large data size and privacy concerns. Conversely, training on a single vehicle would limit the available data and likely result in inferior performance. To address these issues, we propose a federated learning framework for drowsiness detection within a vehicular network, leveraging the YawDD dataset. Our approach achieves an accuracy of 99.2%, demonstrating its promise and comparability to conventional deep learning techniques. Lastly, we show how our model scales using various number of federated clients.
