论文标题
使用实例细分和细心投票的多车道检测
Multi-lane Detection Using Instance Segmentation and Attentive Voting
论文作者
论文摘要
自动驾驶已成为领先的工业研究领域之一。因此，许多汽车公司都提出了半自动驾驶解决方案的半自动驾驶解决方案。在这些解决方案中，车道检测是在自动驾驶汽车的决策过程中起着至关重要的重要作用的重要驾驶员辅助特征之一。已经提出了各种解决方案来检测道路上的车道，范围从使用手工制作的功能到最先进的端到端可训练的深度学习体系结构。这些架构大多数都在交通约束的环境中进行了培训。在本文中，我们为多车道检测提出了一种新颖的解决方案，从精度和速度方面，它的表现优于最先进的方法。为此，与其他基准数据集相比，我们还提供具有更直观的标签方案的数据集。使用我们的方法，我们能够获得99.87％的车道分割精度，在54.53 fps（平均）下运行。
Autonomous driving is becoming one of the leading industrial research areas. Therefore many automobile companies are coming up with semi to fully autonomous driving solutions. Among these solutions, lane detection is one of the vital driver-assist features that play a crucial role in the decision-making process of the autonomous vehicle. A variety of solutions have been proposed to detect lanes on the road, which ranges from using hand-crafted features to the state-of-the-art end-to-end trainable deep learning architectures. Most of these architectures are trained in a traffic constrained environment. In this paper, we propose a novel solution to multi-lane detection, which outperforms state of the art methods in terms of both accuracy and speed. To achieve this, we also offer a dataset with a more intuitive labeling scheme as compared to other benchmark datasets. Using our approach, we are able to obtain a lane segmentation accuracy of 99.87% running at 54.53 fps (average).
