Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155923,
        author = {Sai Keerthana Arun and Dr.Sharvani G S and Girish Rao Salanke},
        title = {Survey on Driver Drowsiness Detection Systems in Advanced Driver Assistance Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {634-642},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155923},
        abstract = {As per the National Highway Traffic Safety Admin- istration(NHTSA) approximately every year close to one lakh road accidents occur because of driver drowsiness. In the year 2017 alone around 1,47,000 people were killed in accidents on the road in India. Each year, millions of people lose their life due to road crashes caused by driver drowsiness. In order to tackle this, people around the world created solutions with the help of different aspects of technology.There are two main measures that are crucial in deciding the degree of drowsiness that the driver is undergoing. Physiological measures are contingent on the impact of the environment on the driver and its affect on various health related signals like ECG and EEG. Behavioural measures largely focus on the driver’s facial features and its impact on the vehicle. This paper presents a thorough survey on existing methodologies that are built using these features as their foundation and showcases which methodology slightly outweighs the others and its future scope.},
        keywords = {Behavioural Measures, Physiological Measures, ADAS,Deep learning,CNN,ECG/EGG},
        month = {},
        }