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Module: Autonomous Vehicles
| Study level | Bachelor 1 | |
| ECTS credits | 3-6 | |
| Study forms | Hybrid or fully online | |
| Module aims | Develop a system-level understanding of autonomous vehicles across ground, aerial and marine domains. By the end of the module, students will be able to explain the core functional stack (perception–planning–control), compare reference architectures (ROS/ROS 2, DDS, AUTOSAR Adaptive, JAUS, MOOS‑IvP), and evaluate trade‑offs between centralized and distributed designs. Students will also assess the role of AI/ML in perception and decision‑making, outline validation & verification (V&V) strategies tied to Operational Design Domains (ODDs), and discuss legal, ethical, and cybersecurity considerations that influence real-world deployment. |
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| Pre-requirements | Motivation to study autonomous systems; recommended basics in programming (Python/C++), signals and control, and electronics/mechatronics. Helpful prior exposure to robotics concepts (kinematics, sensors/actuators) and Linux/ROS tooling. Familiarity with linear algebra and probability will support perception and estimation topics. |
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| Learning outcomes | Knowledge • Explain the Sense–Plan–Act paradigm and the layered autonomy stack (perception, localization, planning, control, coordination). • Describe and contrast middleware/architectures: ROS/ROS 2 (DDS-based), AUTOSAR Adaptive, JAUS, and MOOS‑IvP. • Summarize AI/ML roles in perception (detection, segmentation, tracking) and decision‑making, plus limits and safety implications. • Identify V&V concepts (ODD, coverage, field response) and domain-specific safety standards (e.g., ISO 26262, DO‑178C). Skills • Build a minimal autonomy pipeline in simulation (sensor ingestion → perception → planning → control) 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 (scenario design, criteria for correctness) 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 (OTA, remote control, sensor spoofing) for AV deployment. |
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| Topics | 1. Introduction to autonomous systems and autonomy definitions; Industry 4.0/5.0 context 2. Sense–Plan–Act and data flow in autonomous vehicles; centralized vs. distributed designs; safety & redundancy 3. Reference architectures and middleware: ROS/ROS 2 (DDS), AUTOSAR Adaptive, JAUS, MOOS‑IvP 4. Application domains: ground (Autoware), aerial (PX4/ArduPilot), and marine (MOOS‑IvP); domain challenges 5. AI/ML for perception and decision-making; hybrid model‑based + learning‑based stacks 6. Validation & Verification (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 |
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| 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 | to be added | |
| AI involvement | Explicit list of AI tools and application mtehods | |
| References to literature | to be added | |
| Lab equipment | to be added | |
| Virtual lab | to be added | |
| MOOC course | MOOC Courses hosting for SafeAV, IOT-OPEN.EU Reloaded, and Multiasm grants: http://edu.iot-open.eu/course/index.php?categoryid=3 | |
