Thesis - Generative & Foundational Terramechanics ML Models for Road Classification

We are Geely Technology Europe. A unified European R&D centre within Geely Auto Group, where world-class engineers, developers and

innovators push the boundaries of intelligent mobility. Our mission is to shape the next generation of vehicle architectures,

digital technologies and intelligent systems for global markets. We integrate European customer and regulatory requirements early

in the development process and support multiple brands within the Geely portfolio, including Zeekr, Lynk & Co and Geely. With

nearly two decades of engineering experience in Europe, we continue to build smart, sustainable and user‑centric mobility

solutions.

Vehicle motion and safety functions perform better when they know what road surface the vehicle is on. The road surface can be

classified from signals the vehicle already measures, using supervised time-series classification models trained on labeled

recorded drives. This thesis builds on a previous master's thesis successfully carried out in our department and extends that work

in three directions. First, some road surfaces are rare, hard to reach or costly to collect data on, so data augmentation and

generative modeling on existing recordings could add road surface classes for them and improve the data balance. Second, data from

several vehicle models on similar road surfaces exists, which opens the possibility of one pre-trained model whose learned

representations generalize across vehicle variants despite the domain shift caused by their mechanical architecture. Third,

today's models use a fixed input sequence length (context window), while some road surfaces benefit from a longer one, so a

context length chosen at inference time could serve each road surface better.

• Data analysis and baseline: characterize the class and domain distributions of the available data across road surfaces and

vehicle models, and establish a baseline, evaluation metrics and train/validation/test splits.

• Synthetic data generation techniques: investigate data augmentation and generative modeling methods, such as GANs and other

structured and unstructured style transfer techniques, to produce realistic additional samples from existing recordings, to add

road surface classes that are hard to collect data for and improve the data balance, and assess both the fidelity of the

generated data and its effect on per-class classification performance.

• Towards foundational cross-vehicle generalization: investigate whether a single generalizable road surface classification model

can be pre-trained on data from multiple vehicle models, and how well it transfers under domain shift to vehicles with

different platforms.

• Variable context length: investigate sequence models that accept variable-length input, so the context length can be chosen at

inference time, and study the trade-off between context length, classification accuracy and inference latency for different

road surfaces.

The thesis is expected to show how far synthetic data can extend road surface classification to hard-to-collect road surfaces, how

well one model generalizes across vehicles, and what a variable context length gains, within real-time limits.

• A method for generating synthetic road surface data from existing recordings, with an evaluation of its fidelity and its

benefit for adding hard-to-collect road surface classes and improving the data balance.

• An assessment of the feasibility of a single road surface classification model that generalizes across vehicle models, compared

with vehicle-specific models.

• An approach for a variable context window whose length is chosen at inference time, with an analysis of the accuracy–latency

trade-off.

• Ongoing Master's degree in Data Science, Machine Learning, Complex Adaptive Systems, Vehicle Engineering, Systems and Control,

or a related field

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 Geely 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.

This thesis project is intended for 1-2 students. Applicants may name a preferred partner in their cover letter and must attach a

university transcript.

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

project

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

other channels.