Federated Learning for Drowsiness Detection in Connected Vehicles
This work addresses driver safety by enabling drowsiness detection without centralizing data, though it is incremental as it applies an existing federated learning method to a specific domain.
The paper tackles the problem of driver drowsiness detection in connected vehicles by proposing a federated learning framework to address data size and privacy issues, achieving an accuracy of 99.2% on the YawDD dataset.
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