Object detection holds paramount importance in vehicular applications, serving as a cornerstone for road safety, efficient traffic management, and the evolution of autonomous driving systems. However, while training the object detection models in vehicular applications, the intricacies of data privacy, constrained communication bandwidth, and computational limitations present significant challenges. This thesis aims to leverage the potential of adaptive model compression techniques to augment Federated Learning-based object detection model training in vehicular applications. The central premise is to develop compression strategies (such as pruning, and quantization) that dynamically respond to contextual information, such as weather conditions, traffic density, and time of day, to optimize model training efficiency and accuracy. This integration holds the potential to create more resource-efficient model training that can adapt to changing environments and perform optimally under varying conditions.
This thesis is open for master's students, and the main tasks are as follows:
• Explore relevant existing related literature.
• Formalizing the problem and modeling the solution and
• Setting up a development environment and implementing the proof of concept or prototype.
• Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.
It is required to have:
• Motivated and individual working style
• Very good programming skills in Python.
• Very good machine learning / Deep learning knowledge
I am looking for motivated students interested in working on cutting-edge technologies and vehicular applications area. If you are interested in writing a Bachelor's or Master's Thesis, please feel free to contact me at Ahmad Khalil (ahmad.khalil(at)kom.tu-darmstadt.de). You can email me the following information:
• Your CV or small text about your courses and prior experiences in this area.