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@article{171858,
        author = {Priyank Gupta and Ajay Kumar and Ananya Prasad and Om Mittal and Priyank Gupta and Varchasva Singh},
        title = {Driver Drowsiness Detection with Autonomous Speed Control and Parking System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1247-1250},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171858},
        abstract = {As the prevalence of road accidents continues to rise; the need for advanced safety mechanisms has become imperative. This paper explores the development of a driver drowsiness detection system integrated with autonomous parking functionality. Leveraging state-of-the-art computer vision techniques and machine learning models, the system monitors human behavioral cues such as eye movements, yawning, and head posture to detect signs of fatigue. Upon identifying drowsiness, the system triggers alerts and initiates autonomous vehicle control, guiding the car to a safe parking location. Challenges such as variability in drowsiness expression and limitations of vision-based techniques are addressed. By combining innovative methodologies with hardware integration, this research demonstrates a robust solution for enhancing roadway safety and mitigating accidents caused by driver fatigue.},
        keywords = {Drowsiness Detection, Autonomous Parking, Eye Aspect Ratio (EAR), Yawn Detection, Head Posture Estimation, Computer Vision, Driver Safety.},
        month = {January},
        }