Driving Domain Classification Based on Kernel Density Estimation of Urban Land Use and Road Network Scaling Models

Abstract

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.