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Project Description: 

Reversing with a trailer is a well-known pain point for drivers and a frequent cause of minor collisions and jackknife incidents. Trailer Reverse Assistance (TRA) features depend critically on accurate real-time estimation of the trailer–vehicle articulation (hitch) angle. Current solutions vary widely, from direct sensing at the hitch to camera-based or kinematics-based estimators, each with trade-offs in accuracy, compute cost, user setup burden, and robustness to weather and trailer variability.

Winter conditions (snow, slush, salt, low sun angle) challenge vision systems. Diverse trailer shapes (boat, caravan, box) test model assumptions. Cost and power limits on edge ECUs restrict compute options. A rigorous comparative evaluation and practical recommendation can speed deployment of reliable TRA and related safety functions (jackknife avoidance, trailer stability support, etc.).

Scope

The thesis focuses on developing and evaluating camera/vision-based approaches for real-time trailer hitch angle prediction. Students will investigate different strategies, such as:

• Using visual markers or patterns on the trailer.
• Marker-less estimation using geometric cues segmentation.
• Learning-based angle prediction using deep vision models.

Simple non-vision baselines (for example, kinematic models or basic sensor fusion) may be used for comparison, but the main emphasis is on vision as the primary source of information. The work includes designing a data collection strategy, creating or using a dataset of vehicle–trailer maneuvers, implementing selected vision approaches, and benchmarking them in representative scenarios.

Objectives

• Formulate use cases and basic performance goals for camera-based hitch angle prediction in trailer reversing scenarios.
• Investigate and implement at least one marker-based and one marker-less vision approach for articulation angle estimation.
• Design and apply an evaluation procedure using recorded vehicle–trailer data and suitable reference signals.
• Compare vision methods against simple baseline approaches and analyze strengths, weaknesses, and key failure cases.
• Demonstrate a proof-of-concept implementation that shows the feasibility of real-time prediction on a representative embedded or edge computing platform.

Expected Outcomes

• A structured overview of vision-based techniques for estimating relative pose and angles between a vehicle and a trailer.
• A curated dataset (or extended use of an existing dataset) with synchronized camera data and reference hitch angle measurements for research use.
• Prototypes of vision-based hitch angle estimators (e.g., marker-based and marker-less) that can run in or near real time.
•  A documented benchmarking and analysis of the implemented methods, highlighting trade-offs between accuracy, robustness, and computational effort.

Your skills and background

• Ongoing Master’s degree in Computer Science, Complex Adaptive Systems, System Controls or relevant fields with specialization in Machine Learning (ML)
• Strong background in Computer Vision (CV) and Machine Learning (ML) for perception tasks.
• Knowledge about Vehicle dynamics, Trailer Kinematics, and State Estimation is beneficial
• Strong programming skills in Python, C/C++, or similar languages
• Familiarity with Embedded computing constraints
• Strong Analytical and Problem-solving skills

This thesis project is for 2 students who together need to have the required skills and background. Applicants may specify a preferred partner in their cover letter.

Why you should join Zeekr Tech Eu

We are engineers, developers, and innovators from around the world. Joined together by entrepreneurship, our unique blend of global culture, and a belief in a smarter more sustainable future. At Zeekr Tech Eu we fast-track innovation and transform ideas into pioneering technology solutions, doing your master thesis here is no different. We are convinced that a thesis project is a major contribution to our innovation capabilities and long-term development. You'll have a great opportunity to use your skills and creativity to push the boundaries of what´s possible.

What happens when you apply
If this sounds interesting and you match the requirements, please don't hesitate to submit your application with a CV and cover letter. Shortlisted candidates will be contacted for an interview to further discuss the project's details and expectations. 

Supervisor:  Karthik Prasad, Expert Motion Systems, karthik.prasad@zeekrtech.eu, please contact for more information about the project

Starting date: January 2026

Last application date:  2025-12-03

Apply today. We will perform ongoing selection during the application period. We look forward to hearing from you!

Please note that due to GDPR regulations, we can only accept applications sent through the recruitment system, not via email or other channels.