基于城市土地利用和路网缩放模型核密度估计的驾驶领域分类

Gerrit Brandes, Christian Sieg, Marcel Sander, Roman Henze
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摘要

目前自动驾驶系统的研究重点是特定运行设计域(ODD)中的第 4 级自动驾驶(AD)。来自客户车队运行的测量数据通常用于提取自动驾驶功能测试的场景和 ODD 特征(道路基础设施等)。为确保数据与车辆使用情况相关,需要对数据进行驾驶域分类。一般来说,城市、城外和高速公路域分类可提供具有类似 ODD 特征的数据。公路分类可利用全球导航卫星系统的行驶路线坐标、地图匹配算法和数字地图中存储的道路类别来实现。然而,城市和城外驾驶领域之间的区分则更为复杂,因为住区分类法和行政级别层次结构并非全球一致。因此,本文提出了一种基于地图的驾驶域分类方法。首先,根据城市土地使用密度确定潜在城市区域(PUA),城市土地使用密度根据 OpenStreetMap(OSM)中的土地使用类别确定,然后通过核密度估计进行空间平滑。随后,使用两种路网缩放模型来区分 PUA 的城市域和城外域。最后,对已分类的城市和城外区域的 ODD 特征分布统计进行分析。
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Driving Domain Classification Based on Kernel Density Estimation of Urban Land Use and Road Network Scaling Models
Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas.
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