- Systematic analysis of maneuvers during highway driving
- Development of a control concept for Interactive Longitudinal Control (ILoC)
- Development of concepts for assisted driving in street tunnels
- Development of a mobile radar measurement station
- Development of a replaceable rope winch for the EVITA towing vehicle
- Development of a recuperative brake system for Formula Student race cars
- Development of a test rig for the investigation of wear of passenger car tires
The ADP/ARP should be done as a team work in a group of four to seven students.
Registration is possible either directly as a group or as an individual. Individual applicants are placed on an FZD-internal waiting list until enough participants for a group are available. If groups apply, the ADP/ARP can generally be conducted within the time frame the students wish for, provided timely planning.
Generally, a requirement for the participation in an automotive engineering ADP/ARP is the participation in one or more lectures of FZD. This depends on the topic of the offered ADP/ARP.
No matching topics in the list?
If you can't find a matching topic in the list below you can always apply for a thesis or project using our webform for unsolicited applications. All research associates of FZD will have the chance to see your application.
Webform for unsolicited applications for projects and theses
2023/09/01
In this thesis – in cooperation with TU Darmstadt Racing Team e.V. – the existing CAD model of a formula student race car is to be enriched by measured data (forces, torques, accelerations…) related to driving dynamics and component load. This dataset is to be generated through real experiments with a former FS car.
Supervisor: Dr.-Ing. Melina Lutwitzi
False positive activations of an automatic emergency braking (AEB) system pose a great risk to the driver and other traffic participants, so efforts must be made to demonstrate safety against them, often requiring manual labeling of events.
The target of this ADP is to create a training dataset and investigate the extent to which common machine learning approaches are able to compete with manual labeling of events in recorded data from field tests.
Supervisors: Daniel Betschinske, M.Sc., Malte Schrimpf, M.Sc.
2023/03/03
In this project, the software of an autonomously driving model vehicle is developed. The hardware is already available. At the end of the project, the vehicle should be able to autonomously master a circuit as fast as possible and a further in-depth elective task should be fulfilled (e.g. sign / pedestrian detection, parking,…).
Supervisor: Dr.-Ing. Melina Lutwitzi