• Prediction of behaviour and its storage in maps
    In the context of the RTG i.c.sens, the behavior of objects and phenomena in the environment will be studied in order to describe it and store it in maps.
    Led by: Sester, Monika
    Team: Xu, Yiming
    Year: 2023
    Funding: DFG Graduiertenkolleg i.c.sens
    Duration: 2022-2024
  • Mobile Mapping Bike
    The ikg Mobile Mapping Bike is a supplement to existing mobile mapping systems such as those used at the Institute of Cartography and Geoinformatics, which are traditionally mounted on a car or van. The cargo bike makes it possible to cover or develop remote, winding or inaccessible regions. But the Mobile Mapping Bike can also be used independently, not only to record the surroundings, but also to enrich various other measured values with georeferencing. It can also be used and reconfigured more easily for student projects, as no driving license is required and the software and hardware can be flexibly adapted.
    Led by: Schimansky, Wage, Golze, Feuerhake
    Year: 2023
    Funding: Institutsprojekt
    Duration: fortlaufend
    MobileMappingBike MobileMappingBike
  • 5GAPS - Anwendung im Bereich Urbane Logisitk
    Das Projekt 5GAPS (Access to Public Spaces) entwickelt ein alternatives 5G-mobilfunkgestütztes, zeitlich dynamisches Positionierungssystem auf Basis eines digitalen Zwillings des öffentlichen und halböffentlichen Raums in Form eines dreidimensionalen Rasters. Am ikg werden die Themen 1) Lokalisierung innerhalb und mit Hilfe der 3D-Struktur 2) Visualisierung und Interaktion mit der 3D-Struktur 3) Anwendung der 3D-Struktur für die urbane Logistik bearbeitet.
    Led by: Sester, Monika; Feuerhake, Udo
    Team: Wage, Oskar
    Year: 2022
    Funding: Bundesministerium für Digitales und Verkehr, Förderkennzeichen: 45FGU121_E
  • Traffic Regulator Detection and Identification from Crowdsourced Data
    Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this research is motivated by this fact.
    Team: Zourlidou, Sester
    Year: 2020
  • Zukunftslabor Mobilität
    Im Rahmen des Zukunftslabors Mobilität arbeitet das ikg im Collaborative Research Field 4 am Thema der Mobilitätsdienste. Am CRF 4 sind WissenschaftlerInnen der Disziplinen Dienstleistungsmanagement ( Prof. David Woisetschläger, TU Braunschweig), Wirtschaftsinformatik (Prof. Jörg Müller, TU Clausthal) und Geoinformatik beteiligt. Ausgehend von den Potentialen der hochgradigen Vernetzung intelligenter Fahrzeugsysteme und Infrastrukturen sollen neue Dienstleistungen und Geschäftsmodelle für intelligente Fahrzeuge und (intermodale) Mobilitätslösungen entwickelt, untersucht und demonstriert werden. Im Fokus steht die Anwendung von Methoden für die Exploration von Anforderungen, die Entwicklung und Bewertung von Dienstleistungen für die nutzerspezifische Mobilitätsplanung, Untersuchungen zur Akzeptanz sowie Methoden zur Konzeption, Implementierung und Evaluation digitaler Geschäftsmodelle und hybrider Dienste.
    Led by: Sester
    Team: Koetsier
    Year: 2019
    Funding: MWK Niedersachsen
    Duration: 2019-2024
  • USEfUL
    Due to its location at the center of Europe and the global operating companies, logistics and mobility have always been of outstanding importance in Hanover, a city rebuilt car-friendly after the war. A growing city is associated with increasing mobility and supply needs as well as an individually and systemically caused need of logistics for supply and disposal.
    Team: Wage, Feuerhake
    Year: 2018
    Funding: BMBF: 03SF0547
  • Visual communication to control route choice behavior
    The individual choice of transport modality and route depends on a number of factors. In particular, information about the expected traffic situation is considered important. It should therefore be examined whether the mediation of the current and the anticipated situation on site (including the indication of certain securities) leads to the choice of a different route or even a different modality.
    Team: Fuest, Sester
    Year: 2018
    Funding: DFG-Graduiertenkolleg SocialCars
  • Deep learning of user behavior in road space - particularly in shared spaces
    The project aims to investigate the behaviour of different road users in unregulated spaces, i.e. spaces open to all road users. Existing approaches are based on a given movement model, which describes the individual behaviour as well as the interactive behaviour of different road users.
    Team: Cheng, Sester
    Year: 2018
    Funding: DFG-Graduiertenkolleg SocialCars
    Duration: 2014-2023