Robust lane detection and tracking in challenging scenarios
Robust lane detection and tracking in challenging scenarios
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DOI：
10.1109/tits.2007.908582
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发表时间：
2008-03-01
影响因子：
8.5
通讯作者：
Kim, ZuWhan
中科院分区：
文献类型：
--
作者：
Kim, ZuWhan
车道检测系统是智能交通系统的重要组成部分。我们提出了一个强大的车道检测和跟踪算法来处理具有挑战性的情况下，如车道曲率，磨损车道标记，车道变化，和新兴，结束，合并，和分裂车道。我们首先提出了一个比较研究，以找到一个很好的实时车道线分类。一旦检测完成，车道标记被分组为车道边界假设。我们分别对左侧和右侧车道边界进行分组，以有效地处理合并和拆分车道。提出了一种基于随机样本一致性和粒子滤波的快速、鲁棒性强的假设生成算法，用于真实的实时生成大量假设。基于概率框架对生成的假设进行评估和分组。建议的框架有效地结合了一个基于似然的目标识别算法与马尔可夫风格的过程（跟踪），也可以应用到一般的部分为基础的目标跟踪问题。在局部街道和高速公路上的实验结果表明，该算法是非常可靠的。
A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.