Studies
Open Theses

Open Theses

This page lists open theses for bachelor and master students. 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.

 

 

Are you looking for work that has already been completed?

OPEN BACHELOR THESES

  • Road user tracking in static surveillance video data
    Maps contain important information to navigate and route vehicles. For autonomous vehicles this information about the surrounding has to be highly accurate and current to directly interpret and evaluate the surrounding, measured by sensors. The richer the information is, the better a vehicle can judge the situation, predict next steps and react. The surrounding of the vehicle can significantly influence the driving situation. Which conditions lead to unsafe driving behavior is not always clear. Therefore, it is important to investigate how such situations can be reliably detected, and then search for their triggers. It is conceivable that such insecure situations (e.g near-accidents, u-turns, avoiding obstacles) are reflected, for example, as anomalies in the movement trajectories of road users. Collecting real world traffic data in driving studies is very time consuming and expensive. On the other hand, a lot of roads or public areas are already monitored with video cameras. In addition nowadays more and more of such video data is made publicly available over the internet so that the amount of free but low quality video data is increasing. This research will exploit the use of such kind of opportunistic VGI.
    Leaders: Koetsier, Sester
    Year: 2019
  • Pattern Recognition of Movement Behavior for Intersection Classification using High Frequency GPS Trace Data
    The classification of intersections (assign labels to intersections according to the type of traffic regulator) is motivated by the need for detailed and up-to-date maps. The objective of this thesis focuses on the classification of intersections based on their travel time, which is indicative of the traffic flow and the regulator that rules them (see Fig. on the right, traffic flow under traffic light and priority traffic sign).
    Leaders: Zourlidou
    Year: 2019
  • Crowdsourcing turning restrictions from OpenstreetMap
    Road intersections are locations where different movement patterns are observed: traffic participant go ahead, turn right or left, according both to their needs and most importantly to the traffic restrictions applied everytime at the current location (traffic signs). The aim of this thesis is the implementation of a method, where vehicles trajectories acquired from OpenstreetMap (OSM) are analysed in terms of the turning possibilities that drivers have at each intersection location. Final objective is to find out what kind of turning restrictions are found at those locations, like those shown on the figure right.
    Leaders: Zourlidou
    Year: 2019
  • Trajectory Analysis at Intersections
    Road intersections are locations where different movement patterns are observed: traffic participants go ahead, turn right or left, stop due to traffic lights, wait other traffic participants to finish their manoeuvres, accelerate before traffic light turn "red", decelerate to check the traffic from nearby roads, etc. Moreover, every intersection is regulated variously (traffic-light-controlled, uncontrolled, yield-controlled, stop-controlled), so we expect different movement patterns to be observed at different junctions.
    Leaders: Zourlidou
    Year: 2019
  • Navigation and Field Robotics: Pedestrian Navigation – Obstacle Avoidance with Depth Cameras and Electrical Muscle Stimulation
    This is a topic offered by the Human-Computer Interaction Group. In previous projects the Human-Computer Interaction Group investigated a novel approach to control pedestrians' walking direction for navigation. We showed that controlling the direction with electrical muscle stimulation is possible in outdoornavigation scenarios. As a follow-up project we would like to explore - in a collaborative project with the Institute of Cartography and Geoinformatics (IKG) - how this approach can be used for obstacle avoidance in pedestrian navigation scenarios.
    Leaders: Busch
    Year: 2017

OPEN MASTER THESES

  • Road user tracking in static surveillance video data
    Maps contain important information to navigate and route vehicles. For autonomous vehicles this information about the surrounding has to be highly accurate and current to directly interpret and evaluate the surrounding, measured by sensors. The richer the information is, the better a vehicle can judge the situation, predict next steps and react. The surrounding of the vehicle can significantly influence the driving situation. Which conditions lead to unsafe driving behavior is not always clear. Therefore, it is important to investigate how such situations can be reliably detected, and then search for their triggers. It is conceivable that such insecure situations (e.g near-accidents, u-turns, avoiding obstacles) are reflected, for example, as anomalies in the movement trajectories of road users. Collecting real world traffic data in driving studies is very time consuming and expensive. On the other hand, a lot of roads or public areas are already monitored with video cameras. In addition nowadays more and more of such video data is made publicly available over the internet so that the amount of free but low quality video data is increasing. This research will exploit the use of such kind of opportunistic VGI.
    Leaders: Koetsier, Sester
    Year: 2019
  • On applicability of semantic place discovery algorithms for traffic regulator detection and classification
    The objective of this thesis will focus on the study of vehicle trajectories that can reveal traffic regulations through the recognition of common driving patterns (e.g. collocated events like stops, slow movements, sequences of actions, etc). Vehicle location data and their respective motion measurements (e.g. speed) can be processed in regular intervals for extracting road rules that traffic participants must respect. In other words, by recognizing collective driving behaviour, traffic rules can be automatically mined and mapped providing up-to-date rule-aware maps.
    Leaders: Zourlidou
    Year: 2019
  • Pattern Recognition of Movement Behavior for Intersection Classification using High Frequency GPS Trace Data
    The classification of intersections (assign labels to intersections according to the type of traffic regulator) is motivated by the need for detailed and up-to-date maps. The objective of this thesis focuses on the classification of intersections based on their travel time, which is indicative of the traffic flow and the regulator that rules them (see Fig. on the right, traffic flow under traffic light and priority traffic sign).
    Leaders: Zourlidou
    Year: 2019
  • Crowdsourcing turning restrictions from OpenstreetMap
    Road intersections are locations where different movement patterns are observed: traffic participant go ahead, turn right or left, according both to their needs and most importantly to the traffic restrictions applied everytime at the current location (traffic signs). The aim of this thesis is the implementation of a method, where vehicles trajectories acquired from OpenstreetMap (OSM) are analysed in terms of the turning possibilities that drivers have at each intersection location. Final objective is to find out what kind of turning restrictions are found at those locations, like those shown on the figure right.
    Leaders: Zourlidou
    Year: 2019
  • Trajectory Analysis at Intersections
    Road intersections are locations where different movement patterns are observed: traffic participants go ahead, turn right or left, stop due to traffic lights, wait other traffic participants to finish their manoeuvres, accelerate before traffic light turn "red", decelerate to check the traffic from nearby roads, etc. Moreover, every intersection is regulated variously (traffic-light-controlled, uncontrolled, yield-controlled, stop-controlled), so we expect different movement patterns to be observed at different junctions.
    Leaders: Zourlidou
    Year: 2019
  • Navigation and Field Robotics: Pedestrian Navigation – Obstacle Avoidance with Depth Cameras and Electrical Muscle Stimulation
    This is a topic offered by the Human-Computer Interaction Group. In previous projects the Human-Computer Interaction Group investigated a novel approach to control pedestrians' walking direction for navigation. We showed that controlling the direction with electrical muscle stimulation is possible in outdoornavigation scenarios. As a follow-up project we would like to explore - in a collaborative project with the Institute of Cartography and Geoinformatics (IKG) - how this approach can be used for obstacle avoidance in pedestrian navigation scenarios.
    Leaders: Busch
    Year: 2017
  • Deep Learning: Automatisierte Identifikation von Geländestrukturen am Beispiel von Burgenanlagen
    Mittels Airborne Laserscanning können flächendeckende hochaufgelöste digitale Geländemodelle erstellt werden. Anders als manuell aufgenommene Daten sind diese Daten bis auf eine einfache Klassifikation in Boden und Vegetationspunkte nicht weiter interpretiert. Eine gezielte Interpretation von künstlich-historischen Geländestrukturen muss manuell durchgeführt und mittels Feldbegehung verifiziert werden.
    Leaders: Schulze, Thiemann
    Year: 2017

CONTACT FOR GENERAL QUESTIONS ON THESES

Dipl.-Ing. Frank Thiemann
Address
Appelstraße 9A
30167 Hannover
Building
Room
606
Dipl.-Ing. Frank Thiemann
Address
Appelstraße 9A
30167 Hannover
Building
Room
606