Autonomous Driving

Overview This English-taught, practice-oriented Master's programme prepares you for careers in autonomous driving through project-based, interdisciplinary learning. From the start you work in multi-disciplinary teams to develop a product idea, taking it through real-world development stages with continuous feedback from professors and industry partners. Teamwork, clear communication and active participation are central to the way the programme is run.

Study structure and content The curriculum is organised into six modules that together form a full product development cycle: from concept and analysis of boundary conditions, through technical implementation, to virtual testing of a functioning prototype. Over the first two semesters your team iteratively refines and implements a product concept using agile project-management methods and human-centred design. Core technical topics covered include sensors and actuators, vehicle networking, data processing, artificial intelligence, and human–machine interfaces. In the third semester you complete a Master’s thesis, preferably in collaboration with a company, putting your skills to practical use and enhancing employability.

Why this matters for international students The programme emphasizes hands-on work and close industry ties, giving you experience that employers in automotive, mobility and tech sectors value. Working in interdisciplinary teams mirrors real workplace conditions and develops transferable skills — project leadership, stakeholder communication, and technically grounded problem solving — that will help you transition from study to professional practice.

Program expectations (concise)

• Active participation as an independent contributor within interdisciplinary project teams
• Strong communication and teamwork skills; regular exchange with team members and supervisors
• Willingness to incorporate input from university staff and external industry experts
• Openness to agile project-management and human-centred product-development methods
• Readiness to work across technical areas: sensors/actuators, vehicle networking, data processing, AI, and human–machine interfaces
• Completion of a Master’s thesis in the third semester, ideally in cooperation with a company
• English-language proficiency (programme is taught in English)

Overview This interdisciplinary Master's sequence is built around six consecutive modules that mirror the lifecycle of an autonomous system — from user-centred idea to tested product ready for demonstration. The curriculum combines human-centred design and agile teamwork with core technical subjects such as perception, connectivity, localisation, navigation and system safety. Emphasis is on hands-on team projects that let you apply theoretical concepts directly to a concrete autonomous-use case.

Key modules and learning outcomes

• Module 1 — User-centred Design and Development Processes: learn agile workflows, collaborative team working and human-centred product design; gain an introduction to programming and translate a product idea into an initial concept.
• Module 2 — System Architecture and Safety Concept: derive system architecture and technical requirements from your concept, with a strong focus on product safety; receive foundational instruction in deep learning and V2X (vehicle-to-everything) communication.
• Module 3 — Sensors for Environmental Perception and Data Fusion: develop practical skills in sensor selection, calibration and use; deepen AI knowledge and apply sensor-fusion techniques directly within the team project.
• Module 4 — Vehicle Connectivity and Localisation: acquire localisation algorithms and vehicle-connection methods; study traffic-system simulation, statistics, advanced deep-learning topics and an introduction to vehicle dynamics to situate autonomous agents in real traffic.
• Module 5 — Navigation and Virtual Safeguarding: master SLAM, path/trajectory planning and controller design; learn virtual safeguarding techniques to test and validate systems in simulation; pursue an elective side project to specialise further.
• Module 6 — System Test and Product Launch: integrate and finalise your system to produce a complete demonstration of the chosen use case; focus on rigorous testing and validation and gain insights into product launch processes, with opportunities for deeper study in a scientific colloquium.

Practical outcomes By the end of the program you will be able to conceive and develop an autonomous system end-to-end: apply human-centred design, define safe system architectures, implement perception and sensor-fusion pipelines, perform localisation and navigation, validate behavior in simulation, and prepare a demonstrable product ready for testing and launch. The team-based, project-driven format prepares you for collaborative development environments common in industry and research.

Assessment and course requirements (concise)

• Each module assessed via a portfolio approach.
• Typical deliverables: written reports or graded exercises.
• Every module concludes with an oral presentation.
• Ongoing demonstration of project progress required (live/demo or simulation).
• Team-project participation is a core requirement across modules.
• Opportunities for individual specialisation via side projects and the scientific colloquium.

Graduates are prepared for engineering and development roles in the autonomous mobility sector and related industries. Typical positions include perception and sensor-fusion engineers, AI/deep-learning specialists, vehicle connectivity and localisation engineers, control and trajectory planning engineers, and validation/test engineers focusing on simulation and virtual safeguarding. The programme's product-development and industry collaboration focus also suits roles in systems architecture, product management and integration at OEMs, tier-1 suppliers, mobility startups and tech providers.

The hands-on, safety- and testing-oriented training makes graduates attractive for R&D departments, testing labs and companies working on simulation/virtual validation and safety assurance. The master's also provides a foundation for further research careers (e.g., PhD) in autonomous systems, robotics and related fields.