Lane Detection With Direction Curve Position in Python

An effective lane boundaries projective model (LBPM) and improved detection method in the images captured with a vehicle-mounted monocular camera in complex environments, especially for sharp circular curve lane, is proposed in this paper. Firstly, a lane boundaries projective model is deduced. This lane model can not only express straight-line lane boundaries, but also describe the actual sharp circular curve lane boundaries very well. Secondly, the lane posterior probability function is derived by employing the lane model, the gradient direction feature, the lane likelihood function, and the lane prior information. And then the lane maximum posteriori probability is found out by using the improved particle swarm optimization algorithm. Further the lane boundaries is positioned, and the lane geometric structure, such as the lane left and right boundaries curve radiuses, can be calculated accurately through the lane model. The experimental results show that the proposed lane boundaries projective model and the improved detection method are more effective and accurate for sharp curve lane detection.