论文标题
rasternet:使用激光雷达和架空图像对自由流速度进行建模
RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery
论文作者
论文摘要
道路自由流速度在低交通状况下捕获了典型的车速。建模自由流速度是运输工程的重要问题,并应用于高速公路系统的各种设计,操作,计划和政策决策。不幸的是,收集大规模的历史交通速度数据是昂贵且耗时的。估计自由流速度的传统方法使用基础路段的几何特性,例如等级,曲率,车道宽度,横向间隙和接入点密度,但是对于许多道路,这些特征不可用。我们提出了一种全自动方法Rasternet,用于估算自由流速度而无需明确的几何特征。 rasternet是一个神经网络,使用地理空间一致的栅格结构融合了大规模的顶部图像和空中激光雷德点云。为了支持培训和评估,我们介绍了一个新颖的数据集,结合了整个肯塔基州的路段,间接费用图像和激光点云的自由流速度。我们的方法在基准数据集上实现了最新的结果。
Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applications to a variety of design, operation, planning, and policy decisions of highway systems. Unfortunately, collecting large-scale historical traffic speed data is expensive and time consuming. Traditional approaches for estimating free-flow speed use geometric properties of the underlying road segment, such as grade, curvature, lane width, lateral clearance and access point density, but for many roads such features are unavailable. We propose a fully automated approach, RasterNet, for estimating free-flow speed without the need for explicit geometric features. RasterNet is a neural network that fuses large-scale overhead imagery and aerial LiDAR point clouds using a geospatially consistent raster structure. To support training and evaluation, we introduce a novel dataset combining free-flow speeds of road segments, overhead imagery, and LiDAR point clouds across the state of Kentucky. Our method achieves state-of-the-art results on a benchmark dataset.