Master's thesis: Knowledge Transfer and Federated Learning for Heterogeneous Object Detection Models in Autonomous Vehicles

Background

Autonomous vehicles rely on AI models trained on large-scale, multi-modal sensor data. As vehicle platforms evolve, changes in sensors and hardware often require updating or retraining these models, which is costly and time-consuming. A key challenge is therefore how to efficiently transfer knowledge between models operating under different configurations.

This thesis is part of the research project DREAM – Distributed, Robust and Efficient AI for Autonomous Vehicles. The topic is highly relevant for enabling scalable and efficient AI development in next-generation autonomous driving systems.

Description

Sensor data and AI enable cars to detect objects, understand their environment and make decisions about how to respond. When vehicles are updated and new models are developed, sensors and hardware often change, which in turn also affects the AI models used. One approach would be to create a new AI model from scratch and collect new da...