论文标题
使用复发性神经网络和双重惯例的实时泳道ID估算
Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention
论文作者
论文摘要
获取有关道路车道结构的信息是自动导航的关键步骤。为此，几种方法从不同的角度来解决此任务，例如标记检测或语义车道细分。但是，据我们所知，尚无纯粹基于视觉的端到端解决方案来回答一个确切的问题：如何估计多车道道路或高速公路内当前驱动车道的相对数量或“ ID”？在这项工作中，我们根据双重左右惯例提出了一个实时，仅视觉的（即单眼相机）解决方案。我们通过将最大候选车道数量限制为八个，将此任务解释为分类问题。我们的方法旨在满足低复杂性规格和有限的运行时要求。它利用输入序列固有的时间维度，以改善高复杂性最先进的模型。在具有极端条件和不同途径的具有挑战性的测试集中，我们获得了95％以上的精度。
Acquiring information about the road lane structure is a crucial step for autonomous navigation. To this end, several approaches tackle this task from different perspectives such as lane marking detection or semantic lane segmentation. However, to the best of our knowledge, there is yet no purely vision based end-to-end solution to answer the precise question: How to estimate the relative number or "ID" of the current driven lane within a multi-lane road or a highway? In this work, we propose a real-time, vision-only (i.e. monocular camera) solution to the problem based on a dual left-right convention. We interpret this task as a classification problem by limiting the maximum number of lane candidates to eight. Our approach is designed to meet low-complexity specifications and limited runtime requirements. It harnesses the temporal dimension inherent to the input sequences to improve upon high-complexity state-of-the-art models. We achieve more than 95% accuracy on a challenging test set with extreme conditions and different routes.
