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Automotive IUI


EFFEKT works on solutions for intelligent e-bike fleets. It is a Software Campus project and collaboration between DFKI and Bosch Software Innovations. EFFEKT aims to create a flexible bike fleet of instrumented bikes that will be part of connected and multimodal smart cities.

The approach involves sensor-equipped bicycles and learning techniques that will derive knowledge from collected data and use it to enhance the cycling experience for the new users.

For this purpose EFFEKT builds its own bicycle fleet. Six cargo e-bikes will be available for the students and employees of the Saarland University, free of charge.

GPS, Gyro Accelerometer, Ambient light, Thermometer, Weather sensor, Distance sensor, Camera, Touch and pressure sensor are part of the on board unit that will determine the context of every trip and cyclist.

Project Objectives

  • Fleet management system
  • Multimodal transportation system on campus
  • Automatic community based cycling route generation
  • Real time personalized navigation support for bicycles



Within the scope of software campus project MOBIDA|AD (module for "Big Data" analysis of vehicle environmental data) we develop a reusable test and evaluation environment for the research prototype EyeVIUS (Intelligent Vehicles in Intelligent Urban Spaces). This Research prototype is designed and implemented as a part of dissertation of Mohammad Mehdi Moniri.



SEMA – Drivers' Choices about Semiautonomous Maneuvers: Support for Effective Natural Learning

This two-year research project will analyze the choices that arise for drivers who are using modern cars, which are increasingly capable of executing relatively complex maneuvers semiautonomously. The aim is to adapt the maneuver-executing mechanism and/or the interface with which the driver controls it to create a better natural learning environment for drivers, thereby increasing the safety and effectiveness with which such maneuvers are executed.

The novelty of the project’s approach stems from its application of the “choosability engineering” framework, which has been developed during the last years by Prof. Anthony Jameson, in order to design natural learning environments. Supporting natural learning (i.e., “learning by doing”) is a relatively unexplored approach to decision support that takes into account the fact that most everyday decisions are based largely on implicit rules that have been learned through past experience (as opposed to the thorough processing of information that is available at decision time).


C2X PADE (2011 – 2014)

Im Rahmen dieses Software Campus Projekts wurde eine Entwicklungs- und Evaluationsplattform für neuartige Car2X-Anwendungen geschaffen (C2X PADE – Car2X Platform for Application Development and Evaluation). Diese neuartigen Anwendungen weisen einen hohen Interaktionsgrad auf weshalb ein Fokus auf den bisher vernachlässigten Faktor Mensch gelegt wurde. Gleichzeitig sind aber auch die Anforderungen und Einschränkungen der darunter liegenden Funkkommunikation betrachtet worden, die derzeit hauptsächlich auf Sicherheitsanwendungen ausgerichtet sind.


Die Plattform unterstützt den Entwickler neuartige, interaktive Anwendungen für C2X Netzwerke zu implementieren und diese sowohl im Labor als auch im Versuchsträger unter Realbedingungen zu testen und zu evaluieren. Gleichzeitig können aber auch bereits im Fahrzeug vorhandene Systeme integriert werden. Die entwickelte Plattform nimmt hier dann die Rolle eines Koordinators ein um die unabhängig agierenden Anwendungen im Fahrzeug optimal zu präsentieren. Das System sorgt so für eine stets optimale Benutzung der zur Verfügung stehenden Ein- und Ausgabemodalitäten während der Fahrt auch unter Berücksichtigung des aktuellen Kontextes.