Published Paper Details:

In 2021, a report from the Ministry of Road Transport and Highways Transport Research Wing underscored the alarming toll of road accidents, which claimed the lives of 1,53,972 individuals and injured 3,84,448. The majority of those affected were drivers aged between 18 and 45 years. Additionally, a CDC survey revealed that approximately 1 in 25 adult drivers reported experiencing drowsiness and even falling asleep while operating a vehicle within the past 30 days. The devastating consequences of these accidents highlight the urgent need for effective preventive measures. Transportation companies employing overnight drivers face particularly heightened risks, as nighttime driving often leads to severe fatigue and drowsiness. Consequently, automakers are increasingly implementing driver drowsiness detection systems. While existing systems, such as those employed by Toyota and Audi using ECG machines, have drawbacks like discomfort, there's a growing interest in advanced solutions based on machine learning and deep learning. Proposed systems aim to assess driver fitness and alert them based on fatigue levels, utilizing technologies like webcams for facial monitoring. By implementing such systems on a broader scale and at a manageable cost, the potential to significantly reduce the rate of road accidents is substantial. Moreover, platforms like OLA and Uber could leverage performance analysis modules to monitor drivers' fitness levels and mitigate risks associated with drowsiness effectively.