Master Assignment
[B] Driver Behavior Understanding for Monitoring Fatigue and Anomalous Behavior in Autonomous Driving
Type: Master EE/CS/HMI
Period: TBD
Student: (Unassigned)
If you are interested, please contact :
Project Background:
As autonomous driving technology advances, ensuring the safety and well-being of drivers remains a critical concern. Monitoring driver behavior to detect fatigue and anomalous actions is essential for enhancing the safety of autonomous vehicles and reducing the risk of accidents. This project aims to develop machine learning models capable of understanding driver behavior in real-time, identifying signs of fatigue or unusual actions that could compromise safety, thereby contributing to the future of autonomous driving.
Multiple MSc students can work on this project from the following perspectives:
- Develop driver behavior monitoring models to detect fatigue and identify anomalous behavior from videos.
- Analyze driver behavior patterns in autonomous driving scenarios to improve overall safety and ensure early detection of potential issues.
- Address challenges such as recognizing subtle signs of fatigue, unexpected actions, and the dynamic nature of autonomous driving environments.
Why Join?
- Be part of a cutting-edge project that applies AI to solve real-world challenges in the growing field of autonomous driving.
- Contribute to research that integrates behavior analysis into the development of safer, smarter autonomous driving systems.
- Gain practical experience in video understanding, working with complex datasets and advanced techniques in machine learning and computer vision.
Who Should Apply?
- Students with knowledge of machine learning and computer vision.
- Those interested in behavior analysis