Student Research Projects

Student Research Projects

This page lists open "Student Research Projects". Individual changes to the topics are possible, please contact the respective supervisor.

In addition, there is the possibility of writing a thesis on a topic of your choice. For this purpose, please contact an employee to discuss the topic in detail.

  • Processing of large-scale data sets in the context of autonomous driving
    The research training group i.c.sens has produced large quantities of data to support scientific research in the context of autonomous driving. To this end, multiple cars have been equipped with complex sensor setups for self-localization and mapping, including multiple GNSS systems, stereo cameras and multiple LiDAR systems. In order to enable secondary usage of these data sets and to publish the data set at a later point in time, the data needs to be prepared using established sensor-specific data processing methods or manual data annotation processes (e.g. labeling of images or point clouds towards a reliable ground-truth). The range of possible activities (programming, using a GIS for analyses, manual editing/annotation of data using provided tools and many more) in this research project is wide and can support multiple students at the same time. However, there is a lot of flexibility in determining the specific tasks to carry out (in a meeting before starting the research project).
    Leaders: Kuntzsch, Peters
    Year: 2018
  • Extracting Relevant Features That Determine Collision Avoidance in Shared Spaces
    In distinction to classic traffic designs which, in general, separately dedicate road resources to road users by time or space division, an alternative solution—shared space—has been proposed by traffic engineers. Pedestrians, cyclists, and vehicles interact with each other and self-organize to give or take right-of-way. The safety of shared spaces need to be thoroughly investigated, namely, how road users adapt their speed and/or orientation in the interactions with others in their vicinity to avoid collisions. In order to extract the most relevant features that reflect how a road user adjust his/her motion to avoid potential collisions with others in shared spaces, real-world trajectories will be analysed using statistical and machine learning approaches. For instance, the safe distance may differ significantly across different types of road users. Can we quantify such differences and impacts? Currently, however, user attributes are not yet available in the dataset, which will be incorporated in future work.
    Leaders: Cheng
    Year: 2018
    Sponsors: DFG Graduiertenkolleg SocialCars
  • Ride Vibrations: Towards Comfort-Based Bicycle Navigation
    Due to the wide availability on smartphones, navigation applications are no longer used only for driving in unknown surroundings, but also for everyday travel by bicycle and other modes. In contrast to the car, travel times by bicycle are barely influenced by the respective traffic situation but considerably by the preferred speed (or physical condition) and also to a large extent by the individual route choice made. Especially to avoid situations with low comfort like gradients, many or long stops at traffic lights and badly maintained roads, cyclists vary from the shortest or fastest route. This fact is only indirectly considered in common navigation applications.
    Leaders: Wage, Feuerhake
    Year: 2020