Summary of Federated Learning For Drowsiness Detection in Connected Vehicles, by William Lindskog et al.
Federated Learning for Drowsiness Detection in Connected Vehicles
by William Lindskog, Valentin Spannagl, Christian Prehofer
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel federated learning framework is proposed for driver drowsiness detection in vehicular networks, leveraging the YawDD dataset. The system recognizes visual cues, such as yawning or eye blinking, to determine driver state and generates distributed data. Machine learning techniques like driver drowsiness detection can be employed, but transmitting large amounts of data poses privacy concerns. The framework achieves an accuracy of 99.2%, comparable to conventional deep learning methods, and scales with various numbers of federated clients. This work has implications for ensuring driver readiness and addressing the challenges of distributed data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Driver monitoring systems can help determine a driver’s state by recognizing visual cues like yawning or eye blinking. These systems generate a lot of data that can be used to train machine learning models, such as detecting drowsiness. The problem is that sending this data to a central location for training would be impractical due to its size and concerns about privacy. Training on one vehicle alone wouldn’t work well either because it would limit the amount of data available. To solve these issues, researchers proposed a new way to train models using multiple vehicles, called federated learning. They tested this approach with a dataset called YawDD and found that it worked really well, achieving an accuracy of 99.2%. This technology has the potential to help keep drivers safe by detecting when they’re getting tired. |
Keywords
» Artificial intelligence » Deep learning » Federated learning » Machine learning
