Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

Schwab, Benedikt; Beil, Christof; Kolbe, Thomas H.

doi:10.3390/su12093799

Cited by 25 publications

(21 citation statements)

References 65 publications

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“…The results show that in addition to the age of children, different attention distractors have a significant impact on children's risky behavior in traffic. The reality of models generating different traffic scenarios and predicting the behavior of pedestrians and of individual groups of vulnerable traffic users, such as children, is vital for successful testing of new technologies such is the applicability of autonomous vehicles [22].…”

Section: Introductionmentioning

confidence: 99%

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

et al. 2021

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Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.

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Section: Introductionmentioning

confidence: 99%

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

et al. 2021

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“…This can also lead to partitioning the surface of a road into many small parts to meet the requirements presented in this paper. However, information on usability of surfaces for multiple transportation modes such as a pedestrian crosswalk used by cars as well as pedestrians is necessary for applications such as combined vehicle and pedestrian simulations (Schwab et al 2020). A change of surface materials, attributes such as speed limit or temporary events such as road works would require an even greater fragmentation of surfaces or linear structures into small segments.…”

Section: Discussionmentioning

confidence: 99%

“…Some of these applications would also benefit from consistent and integrated representations of combined transportation infrastructure. This can be important in the context of combined vehicle and pedestrian simulations (Schwab et al 2020). Pedestrian crosswalks, for example, are part of road surfaces that can be used by cars as well as pedestrians.…”

Section: Motivation For Combined Modelling Of Multiple Transportationmentioning

confidence: 99%

“…The bottom image of figure 9 shows the result of a query for all TrafficAreas corresponding to a specific OpenDRIVE reference line (independent of individual functions, highlighted in yellow). Schwab et al (2020) show how information on areal TrafficAreas with multiple function attributes can be used for combined pedestrian and vehicle simulations. This and other streetspace model demos are visualized using the 3DCityDB Web-Map-Client (Yao et al 2018) and are provided for interactive exploration on a corresponding website (http://go.tum.de/300369).…”

Section: Examples and Applicationsmentioning

confidence: 99%

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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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“…In order to address the aforementioned challenges, we propose the method shown in Figure 2. The strategy assumes utilization of dense MLS point cloud data and HD Maps as OpenDRIVE converted to the CityGML standard using the converter r:trån (Schwab et al, 2020). The prerequisite for this method is a georeferenced MLS point cloud at the cm-grade global accuracy.…”

Section: Proposed Pipelinementioning

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.

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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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Spatio-Semantic Road Space Modeling for Vehicle–Pedestrian Simulation to Test Automated Driving Systems

Cited by 25 publications

References 65 publications

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

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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