Vision-based lane recognition under adverse weather conditions using optical flow
Vision-based lane recognition under adverse weather conditions using optical flow
复制标题
使用光流在恶劣天气条件下基于视觉的车道识别
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
Uwe Franke
中科院分区：
文献类型：
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作者：
A. Gern;R. Moebus;Uwe Franke
车道识别是许多驾驶员辅助系统的基础，包括车道偏离警告 (LDW)、将车辆分配到特定车道和全自动驾驶。常见的基于视觉的车道识别系统的一个主要问题是它们对天气的敏感性。特别是在雨雪等恶劣天气条件下驾驶时，很难估计道路路线。白色车道标线与路面的对比度较差，有时标线的颜色会被抵消。此外，视线范围大大减小，导致对车道参数（尤其是曲率）的预测不佳。我们提出的解决方案不仅仅依赖于寻找白色标记。随着时间的推移，将与道路平行的结构关联起来，计算水平光流。然后将其集成到车道识别系统中，估计车辆在车道内的位置和前方道路的曲率参数。该系统可以将雷达或视觉检测到的障碍物可靠地分配到特定车道，即使在恶劣的天气条件下也可以自动横向控制驾驶。
Lane recognition is the basis for many driver assistance systems, including lane departure warning (LDW), the assignment of vehicles to specific lanes and fully autonomous driving. A major problem of common vision-based lane recognition systems is their susceptibility to weather. Especially when driving in adverse weather conditions such as rain or snow it is difficult to estimate the road course. The contrast between the white lane markings and the pavement is poor, sometimes the colors of the markings are negated. Furthermore the range of sight is reduced enormously causing a bad prediction of the lane parameters, particularly the curvature. We present a solution which relies not only on finding white markings. Correlating structures parallel to the road over time the horizontal optical flow is calculated. It is then integrated in the lane recognition system estimating the position of the vehicle within the lane and the curvature parameters of the road ahead. The system allows to assign obstacles detected by radar or vision reliably to specific lanes and to drive laterally controlled autonomously even under adverse weather conditions.