Master Thesis - Reinforcement Learning-based Model Predictive Control for Autonomous Truck Driving
Göteborg, SE, 405 08
Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.
Background
The Efficient Driving team develops on-board software solutions that optimize driving efficiency combined with safety and driver assistance resulting in an optimized and seamless customer experience.
Description of thesis work
The development of autonomous driving systems is at the forefront of improving safety, efficiency, and cost-effectiveness in transportation. Model Predictive Control (MPC) and Reinforcement Learning (RL) techniques are widely used in the domain of autonomous vehicle control. Model Predictive Control (MPC) provides a framework where one can construct rigorous guarantees on safety, while optimizing the operational efficiency over a short future time horizon (<5 seconds). A crucial component of these methods is the so-called terminal cost, that is needed to ensure safe operations, even after the short future horizon. In the case of autonomous vehicles, this term can be intractable to compute by hand as it depends on all surrounding vehicles in the traffic scene. To this end, utilizing the value function from Reinforcement Learning (RL) to learn the terminal cost is a promising alternative.
The thesis work will include:
- Integrate an MPC in a simulation setting for Autonomous Truck driving in highways: Further develop existing software to by integrating an MPC to control an autonomous truck.
- Reinforcement Learning framework for long horizon with traffic: Estimate the value function for long horizon using the RL framework.
- Evaluation in Simulation: Test and evaluate the RL-based MPC in realistic traffic simulation environments, assessing their performance across safety, efficiency, and cost metrics.
Suitable background for the student
This project will use advanced machine learning methods and control to solve problems within autonomous driving. The work will include programming, machine learning, optimization, and vehicle simulation. The work will be carried out at Volvo Group Trucks Technology, Sweden. The thesis is recommended for two students with a strong background in data science, machine learning, and Python.
Ready for the next move?
Are you excited to bring your skills and innovative ideas to the table? We can’t wait to hear from you. Apply today!
Last application date: November 7, 2025.
Location: Gothenburg, Sweden.
Thesis Level: Master.
Language: English.
Starting date: 2026-01-19.
Number of students: Two (2).
Tutor/Supervisor:
- Leo Laine – Volvo GTT
- Deepthi Pathare– Volvo GTT
- Erik Börve– Volvo GTT
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Who we are and what we believe in
We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.
Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.
Group Trucks Technology are seeking talents to help design sustainable transportation solutions for the future. As part of our team, you’ll help us by engineering exciting next-gen technologies and contribute to projects that determine new, sustainable solutions. Bring your love of developing systems, working collaboratively, and your advanced skills to a place where you can make an impact. Join our design shift that leaves society in good shape for the next generation.