Automated driving technologies offer the opportunity to substantially reduce the number of road accidents and fatalities. This requires the development of systems that can handle traffic scenarios more reliable than the human driver. The extreme number of traffic scenarios, though, causes enormous challenges in testing and proving the correct system functioning. Due to its efficiency and reproducibility, the test procedure will involve environment simulations to which the system under test is exposed. A combination of traffic, driving and Vulnerable Road User (VRU) simulation is therefore required for a holistic environment simulation. Since these simulators have different requirements and support various formats, a concept for integrated spatio-semantic road space modeling is proposed in this paper. For this purpose, the established standard OpenDRIVE, which describes road networks with their topology for submicroscopic driving simulation and HD maps, is combined with the internationally used semantic 3D city model standard CityGML. Both standards complement each other, and their combination opens the potentials of both application domains—automotive and 3D GIS. As a result, existing HD maps can now be used by model processing tools, enabling their transformation to the target formats of the respective simulators. Based on this, we demonstrate a distributed environment simulation with the submicroscopic driving simulator Virtual Test Drive and the pedestrian simulator MomenTUM at a sensitive crossing in the city of Ingolstadt. Both simulators are coupled at runtime and the architecture supports the integration of automated driving functions.

<p><strong>Abstract.</strong> Automated driving has received a high degree of public attention in recent years as it will lead to profound changes in mobility, society and urban development. Despite several product announcements from automobile manufacturers and mobility providers, many questions have not yet been answered completely. The need of lane-level HD maps was widely discussed and has been the reason for company acquisitions. HD maps are tailored towards supporting the operation of an automated vehicle. However, the development of this technology also requires road space models, but with a completely different focus and level of detail. Therefore, this article investigates the system development and testing challenges of automated driving. Based on this, requirements of road space models for developing automated driving are derived and gaps to current standards are indicated.</p>

Abstract. A range of different and increasingly accessible acquisition methods, the possibility for frequent data updates of large areas, and a simple data structure are some of the reasons for the popularity of three-dimensional (3D) point cloud data. While there are multiple techniques for segmenting and classifying point clouds, capabilities of common data formats such as LAS for providing semantic information are mostly limited to assigning points to a certain category (classification). However, several fields of application, such as digital urban twins used for simulations and analyses, require more detailed semantic knowledge. This can be provided by semantic 3D city models containing hierarchically structured semantic and spatial information. Although semantic models are often reconstructed from point clouds, they are usually geometrically less accurate due to generalization processes. First, point cloud data structures / formats are discussed with respect to their semantic capabilities. Then, a new approach for integrating point clouds with semantic 3D city models is presented, consequently combining respective advantages of both data types. In addition to elaborate (and established) semantic concepts for several thematic areas, the new version 3.0 of the international Open Geospatial Consortium (OGC) standard CityGML also provides a PointCloud module. In this paper a scheme is shown, how CityGML 3.0 can be used to provide semantic structures for point clouds (directly or stored in a separate LAS file). Methods and metrics to automatically assign points to corresponding Level of Detail (LoD)2 or LoD3 models are presented. Subsequently, dataset examples implementing these concepts are provided for download.

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Abstract. Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available and assure rich semantics and high global accuracy but are represented by rather coarse geometries. While the mentioned downsides curb the usability of these data types for nowadays’ applications, the fusion of both shall maximize their potential. Since testing and developing automated driving functions stands at the forefront of the challenges, we propose a pipeline fusing structured (CityGML and HD Map datasets) and unstructured datasets (MLS point clouds) to maximize their advantages in the automatic 3D road space models reconstruction domain. The pipeline is a parameterized end-to-end solution that integrates segmentation, reconstruction, and modeling tasks while ensuring geometric and semantic validity of models. Firstly, the segmentation of point clouds is supported by the transfer of semantics from a structured to an unstructured dataset. The distinction between horizontal- and vertical-like point cloud subsets enforces a further segmentation or an immediate refinement while only adequately depicted models by point clouds are allowed. Then, based on the classified and filtered point clouds the input 3D model geometries are refined. Building upon the refinement, the semantic enrichment of the 3D models is presented. The deployment of a simulation engine for automated driving research and a city model database tool underlines the versatility of possible application areas.

Abstract. The standard OpenDRIVE is widely used for exchanging road space models in order to simulate the traffic of a city or individual driving situations. For modeling continuous road courses at lane level, OpenDRIVE utilizes its own parametric geometry model. However, violations of continuity requirements due to geometric leaps or kinks can cause the vehicle dynamics simulation to fail when testing vehicle components. But also defective lane predecessor and successor relations can result in an OpenDRIVE dataset not being usable as a reference map for vehicle navigation. Since these geometric, topological, and semantic constraints go beyond the rules encoded in the schema, this article presents a framework and a first implementation for validating OpenDRIVE datasets. As the lane widths are defined parametrically relative to the reference line of the respective road, lane connectivities at road transitions are evaluated using explicit geometries derived from the parametric geometry representations. Moreover, a derived CityGML representation enables a visual inspection of the parametric models to identify unexpected but visible defects. The implemented framework is applied to examine a total of 99 OpenDRIVE datasets, where significant lane gaps were detected in the explicit representation for about 20% of the datasets.