Master Thesis - Trailer angle detection using computer vision
Göteborg, SE, 417 15
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
Safe and efficient reversing of trucks with trailers is a critical challenge in modern freight transport. These vehicle combinations, while common and capable of transporting substantial loads, can encounter reduced maneuverability and stability issues during reversing and shunting operations. Technologies such as active trailer steering and anti-jackknife systems have addressed some of these challenges, but other challenges remain. Accurate detection of the articulation angle between tractor and semitrailer is essential for improving operational safety and enabling advanced driver assistance functions. Recent advances in computer vision and machine learning offer promising solutions for non-intrusive, real-time estimation of articulation angles using camera-based systems.
Description of thesis work
This master’s thesis aims to develop and validate a data-driven estimator that predicts articulation angles from rear-facing camera imagery. The system will be non-intrusive at runtime, requiring no instrumentation on trailers during deployment, and will not rely on prior knowledge of vehicle dimensions or trailer-mounted markers. The approach will leverage machine learning models, including deep-learning architectures, to estimate multiple articulation angles simultaneously and robustly across a variety of trailer types and operating conditions. The expected outcome is a validated angle estimator.
The thesis work will include:
- Model Design & Training: Develop and train machine-learning models (including deep-learning architectures) for robust estimation of the articulation angle from camera imagery.
- Validation: Quantitatively validate the models against archived logs and ground truth, including error analysis and characterization of failure modes.
- Robustness Testing: Evaluate the system under occlusions, low light, lens contamination, and transfer to unseen trailer geometries.
- Benchmarking: Compare the proposed models against classical vision pose estimators and IMU-fused pipelines.
- Integration into Target Hardware: Implement and optimize the developed algorithms on the intended hardware platform, ensuring real-time feasibility and operational reliability.
Suitable background for the student
The ideal candidate should have:
- A strong background in engineering, computer science, or a related field.
- Competencies in machine learning, computer vision, and data analysis are highly desirable.
- Experience with deep learning frameworks (such as TensorFlow or PyTorch), programming (Python or similar), and signal processing will be beneficial.
- Familiarity with vehicle dynamics, sensor fusion, and automotive systems is a plus.
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 7th, 2025.
Location: Gothenburg, Sweden.
Thesis Level: Master.
Language: English.
Starting date: week 4 or week 6 2026.
Number of students: One to Two (1-2)
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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.