Lane detection and tracking modules are now regarded as essential in the development of an Intelligent Transportation System (ITS). Different vision-based algorithms that are being used in autonomous vehicles to determine road lanes are presented, reviewed, and compared in this proposed work. The main components are lane departure, lane tracking, collision avoidance, driver assistance system. Lane departure component assists in keeping the vehicle stable on the desired lane on the road. The front collision avoidance component will detect the road's frontal obstruction and shows up a pre-collision/proximity warning signal. The notice is based on the vehicle's speed and the object's distance from the cars. According to current research, the system can only identify one lane marking set in real-time and is unable to provide extra lanes for assistance in scenarios such as lane closures, road repairs, and car accidents that impede the driving lane. The project proposes a method for determining the marking of two lanes that might be used in conjunction with a detecting system to detect obstructions on the road ahead of the vehicle. This method takes a strong approach to lane detection and performs admirably in a variety of lane types.