2026-09-21T15:50:13Z https://opus4.kobv.de/opus4-uni-passau/oai

oai:kobv.de-opus4-uni-passau:1549 2025-08-26 bibliography:false doc-type:PhDThesis open_access ddc ddc:004 ddc:380

Multimodal Data Space for Cooperative Intelligent Transport Systems Krispin Raich Ing. MSc. 004 380 Modern Cooperative Intelligent Transport Systems (C-ITSs) are comprehensive applications that must cope with a multitude of challenges while meeting strict service and security standards. One of these challenges is a fast, secure, reliable, and universal way to store and exchange data in such a traffic system. Furthermore, multimodal scenarios where different types of vehicles (e.g., cars and Unmanned Aerial System) interact with each other, are increasingly emerging. To overcome these challenges, this thesis presents a set of key innovations to establish a multimodal capable data space for transport application. Therefore, a multimodal optimized geographic model is presented, called SpatialJSON, that is capable of depicting two- and three-dimensional geometries. To accomplish this feat, SpatialJSON extends the popular GeoJSON format with two new data types: area and corridor. Exchanging, managing, and storing data is handled in a novel data-centric middleware, called Large Scale Multimodal Data Processing Middleware for Intelligent Transport Systems (LDPM). This LDPM uses cryptographic- and trust-based schemas to allow secure data exchange and provide data quality assessment. Furthermore, a service architecture is introduced, that fulfils modern service requirements. Trust management is also another essential part of a C-ITS. Hence, a novel scheme to describe traffic related evidence in a multimodal environment is introduced. This schema allows assessing arbitrary traffic related data. This information is then processed in a specialized and modified Bayesian Inference (BI) function. Subsequently, a comprehensive data centric trust management method is introduced. Finally, a use case is presented that relies on the aforementioned technologies to collect data in a hazardous environmental. This data is then distributed and managed via the LDPM, and finally visualized. Universität Passau Passau Innstrasse 29, 94032 Passau Mario Döller Klaus Schöffmann 2025-02-18 2025-02-26 PhDThesis Text urn:nbn:de:bvb:739-opus4-15496 application/pdf eng Creative Commons - CC BY - Namensnennung 4.0 International thesis.doctoral Universität Passau Passau Fakultät für Informatik und Mathematik 1 https://opus4.kobv.de/opus4-uni-passau/oai/container/index/docId/1549 https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1549