This paper is the detailed analysis of lane-detection systems for vehicle accident prevention based on the developments in accident-prevention methods and the image processing and machine learning algorithms to improve the road safety. The critical issues in ensuring proper identification of lane boundaries in different environmental conditions, such as among different lighting conditions, different road geometries and un-favorable weather conditions that strongly influence detection accuracy are dealt with. The methodology is based on a step-wise processing pipeline that fully uses the classical and contemporary methods. The experimental structure combines the Hough transform lane detecting algorithm and Kalman filtering to track the temporal consistency and make the comparative analysis of the traditional detection techniques and the improved hybrid techniques. Although much has already been achieved, more research needs to be done to make more accurate in challenging cases and integrate the driver aid systems in order to make driving safer.  Key components include Gaussian noise reduction for signal enhancement, Canny edge detection with optimized threshold parameters (50-150), and probabilistic Hough Transform with fine-grained parameter space representation (ρ=1 pixel, θ=π/180 radians)

Kalman filter, Hough transform, noise reduction, grayscale conversion