论文标题
特征力：结构多样的车道的数据驱动的车道描述符
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes
论文作者
论文摘要
本文提出了一种新型算法，以检测特征力空间中的道路车道。首先，我们介绍了特征力的概念，这些概念是针对结构上多样化车道的数据驱动的描述符，包括弯曲和直线泳道。为了获得特征力，我们执行包含训练集中所有车道的车道矩阵的最佳排名M近似。其次，我们通过将训练车道聚集在特征力空间中来生成一组候选车道。第三，使用车道候选者，我们通过开发基于锚固的检测网络（称为SIIC-NET）来确定一组最佳的车道。实验结果表明，所提出的算法为结构多样的车道提供了出色的检测性能。我们的代码可在https://github.com/dongkwonjin/eigenlanes上找到。
A novel algorithm to detect road lanes in the eigenlane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candidates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection network, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://github.com/dongkwonjin/Eigenlanes.
