@phdthesis{Raich2025, author = {Raich, Krispin}, title = {Multimodal Data Space for Cooperative Intelligent Transport Systems}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-15496}, school = {Universit{\"a}t Passau}, pages = {xv, 111 Seiten}, year = {2025}, abstract = {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.}, language = {en} }