• Collective Perception - Data Fusion and Visualisation
    The rapid development of data science and machine learning in many research as well as industrial fields has drawn much attention to the fuel of these techniques – the data. In the domain of autonomous driving, the data are mostly collected from different sources which aims to endow the data with more versatility and diversity, and also having a wider coverage in order to get a more complete and accurate perception of the environment. This project aims to improve the reliability and safety of the perception systems for autonomous driving by fusing and analysing the spatiotemporal data from different sensors and different road users that are in the same communication sensor network. In this scenario, the reconstruction of static objects can rely both on asynchronous data from a specific time span of the same sensor as well as the synchronised data from different sensors, the dynamic objects can be tracked based on the later one and auxiliated by the static information obtained. During the fusion process, the accuracies and uncertainties should also be considered and propagated to the final result and then be efficiently visualised in addition to the visualisation of the aggregated environment in order to give the human driver or passenger a correct and precise impression about the current outside-environment so that they can also intervene the driving to fulfil their need without making mistakes.
    Team: Yuan, Sester
    Year: 2020
  • 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
  • Ride Vibrations
    Das Fahrrad als alltägliches Fortbewegungsmittel wird immer beliebter. Doch viele Städte sind darauf noch nicht ausreichend eingestellt. Fahrradwege fehlen, enden plötzlich oder sind schlecht gepflegt. Wo Fahrradwege verlaufen, können Radler meist aus gängigen Kartendiensten ableiten. Wie schnell und wie bequem sie auf diesen Wegen ans Ziel gelangen, aber nicht. Daher entwickeln Mitarbeiter des Instituts für Kartographie und Geoinformatik zusammen mit Geodäsie-Studierenden eine spezielle Navigationsanwendung für Fahrräder, die komfortable Alternativrouten bereitstellen soll. Grundlage ist eine für Android-Smartphones selbst entwickelte App “RideVibes”. Sie zeichnet die Fahrdynamik ohne zusätzlich am Fahrrad angebrachte, spezielle Sensorik auf. Das Smartphone muss dabei lediglich während der Fahrt in einer Halterung am Fahrradlenker fixiert sein.
    Team: Udo Feuerhake, Jens Golze, Christian Koetsier, Oskar Wage
    Year: 2019
    © Karten Tiles von Stamen Design, Kartengrundlage von OSM, Datenüberlagerung von IKG
  • Network Control System of Autonomous Vehicles
    Autonomous systems, such as self-driving cars, unmanned aerial vehicles, autonomous ships, and smart robots, have gained a lot of attention from both academia and industry. Autonomous systems must be capable of planning and executing complex tasks as intended, with limited or no human intervention. They will be exposed to uncertain and unstructured uncertainties arising from modelling errors and external disturbances.
    Leaders: Abdelaal
    Year: 2019
    Sponsors: DFG Graduiertenkolleg i.c.sens
  • 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
    Sponsors: 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
    Sponsors: DFG-Graduiertenkolleg SocialCars
    Lifespan: 2014-2023
  • 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
    Sponsors: BMBF: 03SF0547