Sharp Curve Lane Boundaries Projective Model and Detection
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.
lane detection lane model lane likelihood function lane posterior probability
Yong Chen Mingyi He
School of Software and MicroelectronicsShaanxi Prov. Key Lab. of Inf. Acquisition & ProcessingNorthw Shaanxi Prov. Key Lab. of Inf. Acquisition & Processing School of Electronics and Information Northw
国际会议
IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)
北京
英文
1188-1193
2012-07-25（万方平台首次上网日期，不代表论文的发表时间）