Requirement Analysis of 3d Road Space Models for Automated Driving

Schwab, Benedikt; Kolbe, Thomas H.

doi:10.5194/isprs-annals-iv-4-w8-99-2019

Cited by 17 publications

(15 citation statements)

References 7 publications

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“…These aspects can all be modelled in different levels of accuracy and detail. Applications, such as autonomous driving or pedestrian and vehicle simulations, for example, rely on semantically and geometrically very accurate models of streetspace [7,8], while driver training simulators may focus mostly on highly detailed visualizations [9]. Additionally, streetspace could be interpreted (quite literally) as 'space above a street' where movement of cars, pedestrians, and other traffic members takes place [10].…”

Section: Streetspace Modellingmentioning

confidence: 99%

Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model

Beil

Ruhdorfer²,

Coduro³

et al. 2020

IJGI

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In the context of smart cities and digital twins, three-dimensional semantic city models are increasingly used for the analyses of large urban areas. While the representation of buildings, terrain, and vegetation has become standard for most city models, detailed spatio-semantic representations of streetspace have played a minor role so far. This is now changing (1) because of data availability, and (2) because recent and emerging applications require having detailed data about the streetspace. The upcoming version 3.0 of the international standard CityGML provides a substantially updated data model regarding the transportation infrastructure, including the representation of the streetspace. However, there already exist a number of other standards and data formats dealing with the representation and exchange of streetspace data. Thus, based on an extensive literature review of potential applications as well as discussions and collaborations with relevant stakeholders, seven key modelling aspects of detailed streetspace models are identified. This allows a structured discussion of representational capabilities of the proposed CityGML3.0 Transportation Model with respect to these aspects and in comparison to the other standards. Subsequently, it is shown that CityGML3.0 meets most of these aspects and that streetspace models can be derived from various data sources and for different cities. Models generated compliant to the CityGML standard are immediately usable for a number of applications. This is demonstrated for some applications, such as land use management, solar potential analyses, and traffic and pedestrian simulations.

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Section: Streetspace Modellingmentioning

confidence: 99%

Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model

Beil

Ruhdorfer²,

Coduro³

et al. 2020

IJGI

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show abstract

“…A level crossing for example may share areas used by roads as well as railways, while traffic members obviously cannot switch between the two systems. Virtual testing scenarios in the context of autonomous driving rely on detailed semantic and geometric information of streetspace (Schwab & Kolbe 2019). This requires information on areas used by different traffic members as well as their topological connections.…”

Section: Motivation For Combined Modelling Of Multiple Transportationmentioning

confidence: 99%

“…For this calculation, areas shared by multiple transportation types need to be taken into account without counting them multiple times. Accurate semantic streetspace models can also be relevant in the context of autonomous driving as they can serve as ground truth (Schwab & Kolbe 2019). Information on parts of road surfaces that can also be accessed by pedestrians can be used to identify potential conflict points.…”

Section: Examples and Applicationsmentioning

confidence: 99%

Combined Modelling of Multiple Transportation Infrastructure Within 3d City Models and Its Implementation in Citygml 3.0

Beil

Kolbe

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The development of digital urban twins has led to efforts of multiple cities to gather detailed and highly accurate data on many parts of urban environments, including road and railway infrastructure as well as areas used by pedestrians. This paper presents concepts for representing and segmenting this data semantically, geometrically and topologically ensuring usability for various multimodal applications in the context of digital 3D city models. First, methods for modelling multiple transportation types of several standards such as OpenDRIVE, GDF and INSPIRE are examined, including a discussion of different types of information integration such as functional and topographical representations. Then, concepts proposed to the CityGML Standards Working Group for the CityGML 3.0 Transportation model are presented. This includes detailed methods for modelling multiple transportation modes within a common city model while avoiding redundant geometric representations. A section / intersection concept in combination with links used to model these relations is described. Linear as well as areal models of level crossings, pedestrian crosswalks or areas shared by multiple transportation infrastructure at the same time (e.g. a tramway within a road) are presented. Subsequently, examples and applications that benefit from accurate representations of multiple transportation types are described.

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“…This, however, results in an influx of geodata like Mobile Laser Scanning (MLS) point clouds and High Definition (HD) Maps that depict the road network and its space supporting the navigation and simulation of automated vehicles. Nevertheless, HD Maps may be valid for several test categories of automated driving functions, but as soon as more complex physical sensor effects are demanded for testing, they are not sufficient anymore (Schwab and Kolbe, 2019). For that purpose, more detailed geometrical and semantical representations of real environments are needed.…”

Section: Introductionmentioning

confidence: 99%

“…Depending on the task, each of these applications have different requirements and preferences for 3D models. For example, while maximizing the geometric accuracy of roof surfaces may improve the results of a solar potential analysis (Willenborg et al, 2018), the increased complexity could have a negative impact on the real-time capability of a driving simulation (Schwab and Kolbe, 2019). For the latter, it might be tolerable that the geometric deviation increases quadratically with the distance to the road.…”

Section: Introductionmentioning

confidence: 99%

Plastic Surgery for 3d City Models: A Pipeline for Automatic Geometry Refinement and Semantic Enrichment

Wysocki

Schwab

Hoegner

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

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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.

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Requirement Analysis of 3d Road Space Models for Automated Driving

Cited by 17 publications

References 7 publications

Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model

Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model

Combined Modelling of Multiple Transportation Infrastructure Within 3d City Models and Its Implementation in Citygml 3.0

Plastic Surgery for 3d City Models: A Pipeline for Automatic Geometry Refinement and Semantic Enrichment

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