The problem Road Lane Detection Project solves

A computer vision technique known as road lane detection is used to identify the boundaries of lanes on roads. It can be used in a variety of ways for intelligent transportation systems, driver assistance systems, and autonomous vehicles. some of the ways road lane detection can make existing tasks easier:
Autonomous Driving: By detecting and tracking the lanes on the road, road lane detection can assist autonomous vehicles in safely navigating roads. This enables the car to stay in its lane while making decisions about changing lanes, dodging obstacles, and modifying speed.

Driver Assistance Systems: To give drivers visual cues on the road, road lane detection can be integrated into driver assistance systems. A lane departure warning system, for instance, can warn the driver when the car is about to veer off the road.

Road lane detection is a feature of advanced driver assistance systems (ADAS), which can increase driving comfort and safety. Utilizing lane detection, an adaptive cruise control system can modify the speed of the car.

Challenges we ran into

1)Dealing with shifting lighting conditions, such as shadows and glare, which can lead to false lane detections, is one of the biggest challenges in our project. We overcame this difficulty by normalising the image and adjusting for lighting using image processing techniques.
2)it calls for a sophisticated lane detection algorithm that can precisely model the curves and bends in the road, accurately detecting the curvature of the road found to be difficult for us. To get around this problem, we trained the system to recognise curved lanes using machine learning methods like deep neural networks.
3)Dealing with the variance of road markings, such as the line thickness and colour, which can vary from one road to another, presents another challenge to us. we trained on a broad range of different road markings in order to improve its generalisation ability in order to overcome this difficulty.