Browsing by Author "Schwieger, Volker"

Now showing 1 - 11 of 11

Open Access
Advancing ADAS perception : a sensor-parameterized mmplementation of the GM-PHD filter
(2024) Bader, Christian; Schwieger, Volker
Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA (2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception.
Open Access
Application of Copernicus data for climate-relevant urban planning using the example of water, heat, and vegetation
(2021) Bühler, Michael Max; Sebald, Christoph; Rechid, Diana; Baier, Eberhard; Michalski, Alexander; Rothstein, Benno; Nübel, Konrad; Metzner, Martin; Schwieger, Volker; Harrs, Jan-Albrecht; Jacob, Daniela; Köhler, Lothar; In het Panhuis, Gunnar; Rodríguez Tejeda, Raymundo C.; Herrmann, Michael; Buziek, Gerd
Specific climate adaptation and resilience measures can be efficiently designed and implemented at regional and local levels. Climate and environmental databases are critical for achieving the sustainable development goals (SDGs) and for efficiently planning and implementing appropriate adaptation measures. Available federated and distributed databases can serve as necessary starting points for municipalities to identify needs, prioritize resources, and allocate investments, taking into account often tight budget constraints. High-quality geospatial, climate, and environmental data are now broadly available and remote sensing data, e.g., Copernicus services, will be critical. There are forward-looking approaches to use these datasets to derive forecasts for optimizing urban planning processes for local governments. On the municipal level, however, the existing data have only been used to a limited extent. There are no adequate tools for urban planning with which remote sensing data can be merged and meaningfully combined with local data and further processed and applied in municipal planning and decision-making. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services, and procedures, such as the development of practical technical tools that capture different remote sensing and in-situ data sets for validation and further processing. CoKLIMAx will be used to develop a scalable toolbox for urban planning to increase climate resilience. Focus areas of the project will be water (e.g., soil sealing, stormwater drainage, retention, and flood protection), urban (micro)climate (e.g., heat islands and air flows), and vegetation (e.g., greening strategy, vegetation monitoring/vitality). To this end, new digital process structures will be embedded in local government to enable better policy decisions for the future.
Open Access
Data processing, analysis, and evaluation methods for co-design of coreless filament-wound building systems
(2023) Gil Pérez, Marta; Mindermann, Pascal; Zechmeister, Christoph; Forster, David; Guo, Yanan; Hügle, Sebastian; Kannenberg, Fabian; Balangé, Laura; Schwieger, Volker; Middendorf, Peter; Bischoff, Manfred; Menges, Achim; Gresser, Götz T.; Knippers, Jan
Open Access
Driving environment inference from POI of navigation map : fuzzy logic and machine learning approaches
(2023) Li, Yu; Metzner, Martin; Schwieger, Volker
To adapt vehicle control and plan strategies in a predictive manner, it is usually desired to know the context of a driving environment. This paper aims at efficiently inferring the following five driving environments around vehicle’s vicinity: shopping zone, tourist zone, public station, motor service area, and security zone, whose existences are not necessarily mutually exclusive. To achieve that, we utilize the Point of Interest (POI) data from a navigation map as the semantic clue, and solve the inference task as a multilabel classification problem. Specifically, we first extract all relevant POI objects from a map, then transform these discrete POI objects into numerical POI features. Based on these POI features, we finally predict the occurrence of each driving environment via an inference engine. To calculate representative POI features, a statistical approach is introduced. To composite an inference engine, three inference systems are investigated: fuzzy inference system (FIS), support vector machine (SVM), and multilayer perceptron (MLP). In total, we implement 11 variants of inference engine following two inference strategies: independent and unified inference strategies, and conduct comprehensive evaluation on a manually collected dataset. The result shows that the proposed inference framework generalizes well on different inference systems, where the best overall F1 score 0.8699 is achieved by the MLP-based inference engine following the unified inference strategy, along with the fastest inference time of 0.0002 millisecond per sample. Hence, the generalization ability and efficiency of the proposed inference framework are proved.
Open Access
Elementary error model applied to terrestrial laser scanning measurements: study case arch dam Kops
(2020) Kerekes, Gabriel; Schwieger, Volker
All measurements are affected by systematic and random deviations. A huge challenge is to correctly consider these effects on the results. Terrestrial laser scanners deliver point clouds that usually precede surface modeling. Therefore, stochastic information of the measured points directly influences the modeled surface quality. The elementary error model (EEM) is one method used to determine error sources impact on variances-covariance matrices (VCM). This approach assumes linear models and normal distributed deviations, despite the non-linear nature of the observations. It has been proven that in 90% of the cases, linearity can be assumed. In previous publications on the topic, EEM results were shown on simulated data sets while focusing on panorama laser scanners. Within this paper an application of the EEM is presented on a real object and a functional model is introduced for hybrid laser scanners. The focus is set on instrumental and atmospheric error sources. A different approach is used to classify the atmospheric parameters as stochastic correlating elementary errors, thus expanding the currently available EEM. Former approaches considered atmospheric parameters functional correlating elementary errors. Results highlight existing spatial correlations for varying scanner positions and different atmospheric conditions at the arch dam Kops in Austria.
Open Access
Estimating control points for B-spline surfaces using fully populated synthetic variance−covariance matrices for TLS point clouds
(2021) Raschhofer, Jakob; Kerekes, Gabriel; Harmening, Corinna; Neuner, Hans; Schwieger, Volker
A flexible approach for geometric modelling of point clouds obtained from Terrestrial Laser Scanning (TLS) is by means of B-splines. These functions have gained some popularity in the engineering geodesy as they provide a suitable basis for a spatially continuous and parametric deformation analysis. In the predominant studies on geometric modelling of point clouds by B-splines, uncorrelated and equally weighted measurements are assumed. Trying to overcome this, the elementary errors theory is applied for establishing fully populated covariance matrices of TLS observations that consider correlations in the observed point clouds. In this article, a systematic approach for establishing realistic synthetic variance–covariance matrices (SVCMs) is presented and afterward used to model TLS point clouds by B-splines. Additionally, three criteria are selected to analyze the impact of different SVCMs on the functional and stochastic components of the estimation results. Plausible levels for variances and covariances are obtained using a test specimen of several dm—dimension. It is used to identify the most dominant elementary errors under laboratory conditions. Starting values for the variance level are obtained from a TLS calibration. The impact of SVCMs with different structures and different numeric values are comparatively investigated. Main findings of the paper are that for the analyzed object size and distances, the structure of the covariance matrix does not significantly affect the location of the estimated surface control points, but their precision in terms of the corresponding standard deviations. Regarding the latter, properly setting the main diagonal terms of the SVCM is of superordinate importance compared to setting the off-diagonal ones. The investigation of some individual errors revealed that the influence of their standard deviation on the precision of the estimated parameters is primarily dependent on the scanning distance. When the distance stays the same, one-sided influences on the precision of the estimated control points can be observed with an increase in the standard deviations.
Open Access
Holistic quality model and assessment : supporting decision-making towards sustainable construction using the design and production of graded concrete components as an example
(2022) Frost, Deniz; Gericke, Oliver; Di Bari, Roberta; Balangé, Laura; Zhang, Li; Blagojevic, Boris; Nigl, David; Haag, Phillip; Blandini, Lucio; Jünger, Hans Christian; Kropp, Cordula; Leistner, Philip; Sawodny, Oliver; Schwieger, Volker; Sobek, Werner
This paper describes a holistic quality model (HQM) and assessment to support decision-making processes in construction. A graded concrete slab serves as an example to illustrate how to consider technical, environmental, and social quality criteria and their interrelations. The evaluation of the design and production process of the graded concrete component shows that it has advantages compared to a conventional solid slab, especially in terms of environmental performance. At the same time, the holistic quality model identifies potential improvements for the technology of graded concrete. It will be shown that the holistic quality model can be used to (a) consider the whole life cycle in decision-making in the early phases and, thus, make the complexity of construction processes manageable for quality and sustainability assessments and (b) make visible interdependencies between different quality and sustainability criteria, to help designers make better-informed decisions regarding the overall quality. The results show how different quality aspects can be assessed and trade-offs are also possible through the understanding of the relationships among characteristics. For this purpose, in addition to the quality assessment of graded concrete, an overview of the interrelations of different quality characteristics is provided. While this article demonstrates how a HQM can support decision-making in design, the validity of the presented evaluation is limited by the data availability and methodological challenges, specifically regarding the quantification of interrelations.
Open Access
Lane-level map-aiding approach based on non-lane-level digital map data in road transport security
(2021) Luz, Philipp; Zhang, Li; Wang, Jinyue; Schwieger, Volker
To prevent terror attacks in which trucks are used as weapons as happened in Nice or Berlin in 2016, the European Project Autonomous Emergency Maneuvering and Movement Monitoring for Road Transport Security (TransSec) was launched in 2018. One crucial point of this project is the development of a map-aiding approach for the localization of vehicles on digital maps, so that the information in digital map data can be used to detect prohibited driving maneuvers, such as offroad or wrong-way drivers. For example, a lane-level map-aiding approach is required for wrongway driver detection. Navigation Data Standard (NDS) is one of the worldwide map standards developed by several automobile manufacturers. So far, there is no lane-level NDS map covers a large area, therefore, it was decided to use the latest available NDS map without lane level accuracy. In this paper, a lane-level map-aiding approach based on a non-lane-level NDS map is presented. Due to the inaccuracy of vehicle position and digital map the map-aiding does not always provide the correct results, so probabilities of off-road and wrong-way diver detection are estimated to support risk estimation. The performance of the developed map-aiding approach is comprehensively evaluated with both real and simulated trajectories.
Open Access
Method of development of a new regional ionosphere model (RIM) to improve static single-frequency precise point positioning (SF-PPP) for Egypt using Bernese GNSS software
(2023) Abdallah, Ashraf; Agag, Tarek; Schwieger, Volker
Due to the lack of coverage of IGS in Africa, especially over North Africa, and the construction revolution of infrastructure in Egypt, a geodetic CORS stations network was established in 2012. These CORS stations are operated by the Egyptian Surveying Authority (Egy. SA) and cover the whole of Egypt. The paper presents a fully developed regional ionosphere model (RIM) depending on the Egyptian CORS stations. The new model and the PPP solution were obtained using Bernese GNSS V. 5.2 software. An observation data series of eight days (DOY 201-208)/2019 was used in this study. Eighteen stations were used to develop the RIM model for each day; fifteen stations were used to validate the new RIM model. A static SF-PPP solution was obtained using the CODE-GIM and RIM models. Comparing the outcomes to the reference network solution, based on the recently developed RIM model, the solution showed a mean error of 0.06 m in the East direction, 0.13 m in the North direction, and 0.21 m in the height direction. In the East, North, and height directions, this solution improves the SF-PPP result achieved by the Global Ionosphere Maps (CODE-GIM) model by 60%, 68%, and 77%, respectively.
Open Access
Modelling vegetation health and its relation to climate conditions using Copernicus data in the City of Constance
(2024) Khikmah, Fithrothul; Sebald, Christoph; Metzner, Martin; Schwieger, Volker
Monitoring vegetation health and its response to climate conditions is critical for assessing the impact of climate change on urban environments. While many studies simulate and map the health of vegetation, there seems to be a lack of high-resolution, low-scale data and easy-to-use tools for managers in the municipal administration that they can make use of for decision-making. Data related to climate and vegetation indicators, such as those provided by the C3S Copernicus Data Store (CDS), are mostly available with a coarse resolution but readily available as freely available and open data. This study aims to develop a systematic approach and workflow to provide a simple tool for monitoring vegetation changes and health. We built a toolbox to streamline the geoprocessing workflow. The data derived from CDS included bioclimate indicators such as the annual moisture index and the minimum temperature of the coldest month (BIO06). The biophysical parameters used are leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR). We used a linear regression model to derive equations for downscaled biophysical parameters, applying vegetation indices derived from Sentinel-2, to identify the vegetation health status. We also downscaled the bioclimatic indicators using the digital elevation model (DEM) and Landsat surface temperature derived from Landsat 8 through Bayesian kriging regression. The downscaled indicators serve as a critical input for forest-based classification regression to model climate envelopes to address suitable climate conditions for vegetation growth. The results derived contribute to the overall development of a workflow and tool for and within the CoKLIMAx project to gain and deliver new insights that capture vegetation health by explicitly using data from the CDS with a focus on the City of Constance at Lake Constance in southern Germany. The results shall help gain new insights and improve urban resilient, climate-adaptive planning by providing an intuitive tool for monitoring vegetation health and its response to climate conditions.
Open Access
Monitoring of the production process of graded concrete component using terrestrial laser scanning
(2021) Yang, Yihui; Balangé, Laura; Gericke, Oliver; Schmeer, Daniel; Zhang, Li; Sobek, Werner; Schwieger, Volker