Driving while drowsy is one of the most common causes of car accidents and fatalities. The vast majority of conventional methods are based on the body, the mind, or a vehicle. As a result, this work creates a low cost, accurate, and real time driver sleepiness detection system. A camera records the video in the designed system, and image processing algorithms identify the drivers face in each frame. Facial landmarks on the detected face in each frame. Facial landmarks on the detected face are identified, ratio of eyes, mouth opening, nose length are calculated, and tiredness is determined based on their values using developed adaptive thresholding. Machine learning algorithms have also been utilized offline. A lane detection system is a crucial component of numerous technologically advanced transportation systems. lane boundaries are determined with the help of a camera mounted in the front that takes the picture of the road. In this study the method of dividing the video into sub-images and creating image features for each one is used to determine whether or not there are lanes on the roads. Lane markers can be found on the road in a variety of ways.