Thesis Work: 30 credits - End-to-end learning for self-driving heavy duty vehicles

Introduction
A thesis project at Traton is an excellent way of making contacts for your future working life. Many of our current employees began their careers with a thesis project.

Background
Autonomous driving technologies are rapidly evolving and are expected to transform the transportation industry. Traditional approaches to autonomous driving often separate perception, prediction, and planning into distinct modules. However, recent research explores end-to-end trainable networks, where deep learning models directly map sensory input (e.g., camera, lidar, radar) to safe trajectories. This paradigm has the potential to simplify system design, improve adaptability, and leverage large-scale data. At the same time, it introduces challenges in interpretability, safety, and the ability to generalize to diverse real-world conditions. For heavy-duty vehicles such as trucks, these challenges become even more pronounced due to their size and complex dynamics.

Objective
Investigate, design, and evaluate end-to-end trainable neural network architectures for self-driving heavy duty-vehicles.

Job description
The thesis can roughly be divided into the following sub-task:

• Survey and summarize related literature.
• Propose and implement a baseline end-to-end trainable network using publicly available datasets.
• Fine-tune and extend the baseline using data collected in-house
• Evaluate performance.
• Summarize the findings into a thesis report.

Education
Master (civilingenjör) in computer science, robotics, engineering physics, electrical engineering, or applied mathematics, preferably with specialization in artificial intelligence algorithms, control theory, optimal control or optimization. Knowledge of programming, and reinforcement learning are a plus.

Number of students: 1-2
Start date: January 2026  
Estimated time needed: 20 weeks

Contact person and supervisor
Cristina Cipriani, Research & Development Engineer in Autonomous Motion: 
cristina.cipriani@scania.com, +46 76 506 9935

Yunus Emre Sahin, Research & Development Engineer in Autonomous Motion: 
yunus-emre.sahin@scania.com, +46 85 538 1547

Application
Enclose CV, cover letter and transcript of records.

A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the day specified.