R Palafox Pablo, Betz Johannes, Nobis Felix, Riedl Konstantin, Lienkamp Markus
Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Garching bei München, Germany.
Sensors (Basel). 2019 Jul 22;19(14):3224. doi: 10.3390/s19143224.
Typically, lane departure warning systems rely on lane lines being present on the road.However, in many scenarios, e.g., secondary roads or some streets in cities, lane lines are eithernot present or not sufficiently well signaled. In this work, we present a vision-based method tolocate a vehicle within the road when no lane lines are present using only RGB images as input.To this end, we propose to fuse together the outputs of a semantic segmentation and a monoculardepth estimation architecture to reconstruct locally a semantic 3D point cloud of the viewed scene.We only retain points belonging to the road and, additionally, to any kind of fences or walls thatmight be present right at the sides of the road. We then compute the width of the road at a certainpoint on the planned trajectory and, additionally, what we denote as the fence-to-fence distance.Our system is suited to any kind of motoring scenario and is especially useful when lane lines arenot present on the road or do not signal the path correctly. The additional fence-to-fence distancecomputation is complementary to the road's width estimation. We quantitatively test our methodon a set of images featuring streets of the city of Munich that contain a road-fence structure, so asto compare our two proposed variants, namely the road's width and the fence-to-fence distancecomputation. In addition, we also validate our system qualitatively on the Stuttgart sequence of thepublicly available Cityscapes dataset, where no fences or walls are present at the sides of the road,thus demonstrating that our system can be deployed in a standard city-like environment. For thebenefit of the community, we make our software open source.
通常，车道偏离预警系统依赖于道路上存在车道线。然而，在许多场景中，例如二级公路或城市中的一些街道，车道线要么不存在，要么标识不够清晰。在这项工作中，我们提出了一种基于视觉的方法，当没有车道线时，仅使用RGB图像作为输入来在道路内定位车辆。为此，我们建议将语义分割和单目深度估计架构的输出融合在一起，以局部重建所观察场景的语义三维点云。我们只保留属于道路的点，此外，还保留可能存在于道路两侧的任何类型的围栏或墙壁的点。然后，我们计算规划轨迹上某一点处道路的宽度，此外，还计算我们所称的围栏到围栏的距离。我们的系统适用于任何类型的驾驶场景，当道路上没有车道线或车道线不能正确标识路径时尤其有用。额外的围栏到围栏距离计算是对道路宽度估计的补充。我们在一组以慕尼黑市街道为特色且包含道路围栏结构的图像上对我们的方法进行了定量测试，以便比较我们提出且计算道路宽度和围栏到围栏距离的两个变体。此外，我们还在公开可用的Cityscapes数据集中的斯图加特序列上对我们的系统进行了定性验证，该序列中道路两侧没有围栏或墙壁，从而证明我们的系统可以部署在类似标准城市的环境中。为了社区的利益，我们将我们的软件开源。
