Background

Autonomous vehicles rely on AI models and vast amounts of multi-modal sensor data—including vehicle control signals, GPS, lidar, radar, and camera inputs—to perceive and navigate their environment. 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 data each time the vehicle platform is updated. A more efficient solution would be to transfer knowledge between models with varying architectures. In this Master’s thesis project, we aim to investigate how knowledge distillation mechanisms can facilitate knowledge transfer between diverse models and hardware setups, ensuring that learning can continue even when architectures change. The work will use the Zenseact Open Dataset and also explore knowledge distillation in a federated learning context.

Description

In this master thesis project, you will focus on investigating knowledge distillation as a method to support knowledge transfer between diverse models and hardware setups in autonomous vehicles. Specifically, you will:

• Explore how knowledge can be efficiently transferred between AI models with different architectures.
• Evaluate techniques for updating AI models when vehicle sensors or hardware change, without the need for full retraining or collecting extensive new datasets.
• Analyze the effectiveness of the proposed approach through experiments using multi-modal sensor data, including vehicle control signals, geographical positions, and lidar, radar, and camera measurements.
• Develop and experiment with a federated learning framework that incorporates knowledge distillation to maintain model performance and adaptability in real-time, large-scale deployments.

Key Responsibilities

1. Design and Implement Knowledge Transfer Frameworks: Develop and optimize strategies for transferring knowledge between heterogeneous AI models in both simulations and real-world driving scenarios.
2. Simulation, Evaluation, and Safety Oversight: Create and manage diverse simulation environments, monitor agent behavior, and ensure safe and efficient decision-making in autonomous systems.
3. Collaboration, Integration, and Continuous Improvement: Work across AI architectures and vehicle platforms, coordinate among system components, mentor team members, and refine methods for performance and efficiency.

Qualifications

We are looking for highly motivated students with a good general background in machine learning and computer vision. The following skills would be essential:

• Deep learning
• Federated learning (would be a bonus)
• Python programming
• Reading scientific papers
• Handling complex systems

Terms

• Industry supervisor:
  • Henrik Abrahamsson, PhD (henrik.abrahamsson@ri.se)
  • Sima Sinaei, PhD (sima.sinaei@ri.se)
• Division, department: Digital Systems division, Industrial Systems department
• Location: RISE, Kista, Stockholm
• Application deadline: November 15^th, 2025
• Starting date: As soon as possible, not later than December 1^st, 2025.
• Credits: 30 points
• Compensation: 30 000 SEK upon a successful completion of a high-quality thesis.

Welcome with your application!