| Study level | Bachelor |
|---|---|
| ECTS credits | 1 ECTS |
| Study forms | Hybrid or fully online |
| Module aims | The aim of the module is to introduce the fundamental concepts, architectures and application domains of autonomous vehicles across ground, aerial and marine systems. The course develops students’ system-level understanding of the autonomy stack from perception and localisation to planning and control, highlighting the role of AI, safety and basic verification considerations in real-world deployment. |
| Pre-requirements | Interest in autonomous systems and basic knowledge of programming, signals and control, and electronics or mechatronics. Prior exposure to robotics concepts and Linux/ROS environments, as well as familiarity with linear algebra and probability, is recommended. |
| Learning outcomes | Knowledge • Explain the Sense–Plan–Act paradigm and the layered autonomy stack. • Describe and contrast middleware/architectures. • Summarize AI/ML roles in perception and decision-making, plus limits and safety implications. • Identify V&V concepts and domain-specific safety standards. Skills • Build a minimal autonomy pipeline in simulation and tune it for a given ODD. • Integrate modules via publish/subscribe interfaces and evaluate latency, determinism, and fault-tolerance trade-offs. • Design basic experiments to validate algorithms and interpret results. Understanding • Reason about distributed vs. centralized architectures and their impact on scalability and reliability. • Appraise governance, legal/ethical constraints, and cybersecurity risks for AV deployment. |
| Topics | 1. Introduction to autonomous systems and autonomy definitions 2. Sense–Plan–Act and data flow in autonomous vehicles; centralized vs. distributed designs; safety & redundancy 3. Reference architectures and middleware: ROS/ROS2 (DDS), AUTOSAR Adaptive, JAUS, MOOS-IvP 4. Application domains: ground, aerial, and marine; domain challenges 5. AI/ML for perception and decision-making; hybrid model-based, learning-based stacks 6. Validation and Verification introduction (ODD, coverage, field response); simulation, SIL/HIL; safety standards 7. Governance, legal and ethical frameworks for autonomy 8. Cybersecurity for autonomous systems: electronics/firmware, communication, control, operations |
| Type of assessment | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation. |
| Learning methods | Lecture — Conceptual foundations (architectures, middleware, SPA, safety/V&V, governance) with case studies from ground, aerial, and marine domains. Lab works — Hands-on exercises in simulation (ROS2/Autoware/PX4 or MOOS-IvP) to assemble perception-planning-control pipelines and evaluate behavior. Individual assignments — Focused mini-projects (e.g., perception module, path planner, DDS QoS study) with short reports on design and results. Self-learning — Guided readings and video demos on standards and frameworks; independent experimentation to deepen understanding of chosen topics. |
| AI involvement | Deep learning for perception (object detection, semantic segmentation, tracking); learning-based prediction; SLAM and sensor fusion with ML components; reinforcement/behavior-tree hybrids for decision-making; data-centric evaluation in simulation. |
| Recommended tools and environments | ROS/ROS2, MOOS-IvP, Autoware, PX4/ArduPilot |
| Verification and Validation focus | |
| Relevant standards and regulatory frameworks | ISO 26262, DO-178C, AUTOSAR, JAUS |