摘要
车道检测算法的研究是智能车辆基于道路标识线或边界信息自主导航的首要环节。根据道路先验知识和驾驶员视觉处理经验,将道路图像分为近景和远景区域,近景区使用直线模型拟合车道线,远景区切换直线模型或三次曲线模型匹配车道线。融合道路图像的梯度幅值、梯度方向和灰度特征信息,建立概率判别函数,采用基于遗传算法操作的改进粒子群优化算法,快速搜索关于概率函数的最优模型参数,实现对车道的检测。对实际道路图像的试验结果表明,在路面存在阴影、光照不均匀、车辆遮挡以及车道标识线污损情况下,该算法都能很好地识别车道,具有很强的鲁棒性。
It is principal to detect lane robustly and rapidly for intelligent vehicle based on the information of road marking or road region.The road image is divided into tow parts called near area and far area based on pre-knowledge and human visual experience.A linear model is adopted to fit the lane marking in near area,while in far area,the lane marking with the lane model is switched between linear model and cubic curve model.Combined with the gradient value,gradient direction and gray information,discriminant function of the probability is derived.Then the improved Particle Swarm Optimization（PSO） algorithm combined with genetic algorithm operators is used to quickly search the optimal model parameter of the discriminant function to implementation lane detection.The results of the real road image experiment show the proposed method can robustly and rapidly detect the lane markings even if there are some interference factors in the road such as shadow,non-uniform illuminance,vehicle barrier and soiled lane boundaries.
出处
《光电工程》
CAS
CSCD
北大核心
2012年第1期17-23,共7页
Opto-Electronic Engineering
基金
军械工程学院原始创新基金(YSCX004)
