Research on lane detection algorithm for large curvature curve

Shihe Tian, Zhian Zhang, X. Huang
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Abstract

Existing lane line detection algorithm for identification of a straight line in good condition, to solve the problem of curve, however, failed to find a good strategy, especially in large curvature of curve, the visual field to extract the lane line produces by the two become one, resulting in a wrong calculation, in the case of real vehicle test, bend by camera height, visual field, etc. The indoor robot car is used as the carrier for the test, and a turning strategy is proposed to recognize the lane line at the corner, and the lane line detection algorithm based on sliding window is improved to make it less affected by the environment. The algorithm is simple and efficient, which is suitable for the indoor robot car visual line inspection. The experimental results show that the lane detection algorithm proposed in this paper improves the passing rate and stability of the robot car under large area rate curves.
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大曲率曲线车道检测算法研究
现有的车道线检测算法用于识别状态良好的直线,解决弯道问题,然而,未能找到良好的策略,特别是在曲率较大的弯道中,视野提取车道线产生的两点合二为一,导致计算错误,在实车测试的情况下,弯道受摄像机高度、视野等影响。以室内机器人汽车为载体进行测试,提出了一种转角车道线识别的转弯策略,并改进了基于滑动窗的车道线检测算法,使其受环境影响较小。该算法简单高效,适用于室内机器人汽车视觉线检测。实验结果表明,本文提出的车道检测算法提高了机器人汽车在大面积速度曲线下的通过率和稳定性。
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