In developing a drowsiness driver detector, I gained hands-on experience with various technical aspects, including computer vision and machine learning. I implemented algorithms for real-time facial recognition and eye tracking using OpenCV, which allowed for the detection of drowsiness indicators such as eye closure duration and head position. I also learned to preprocess image data for optimal accuracy and integrate the system with alert mechanisms to provide timely warnings to the driver. Additionally, I gained insights into the importance of user interface design for effective interaction and feedback. Overall, this project enhanced my understanding of sensor integration and the application of AI in safety-critical systems.