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--- en:safeav:curriculum:as [2025/09/24 16:13] – created larisas
+++ en:safeav:curriculum:as [2025/11/05 10:56] (current) – airi
@@ Line 1: / Line 1: @@
 ====== Module: Autonomous Vehicles ======
-| **Study level**                 | Bachelor 1                                                                                                                                                ||
-| **ECTS credits**                | 3-6                                                                                                                                                       ||
+^ **Study level** | Bachelor |
-| **Study forms**                 | Hybrid or fully online                                                                                                                                    ||
+^ **ECTS credits** | 1 ECTS |
-| **Module aims**                 | to be added                                                                                                                                               ||
+^ **Study forms** | Hybrid or fully online |
-| **Pre-requirements**            | Motivation to study AV, recommended to have basics on programming, electronics and mechatronics                                                           ||
+^ **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. |
-| **Learning outcomes**           | After completing this module (for every topic listed below), the student:\\  - knows x\\  - knows y\\  - understands z\\  - can w                         ||
+^ **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. |
-| ** Topics **                    | __Topic AV1 __ (1 ECTS) \\ \\ __Topic AV2 __ (2 ECTS)) \\ \\ __Topic AV3 __ (2 ECTS)\\ \\ __Topic AV4 __ (1 ECTS) \\                                      ||
+^ **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. |
-| **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     ||
+^ **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 |
-| **Learning methods**            | to be added                                                                                                                                               ||
+^ **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. |
-| **AI involvement**              | Explicit list of AI tools and application mtehods                                                                                                      |   |
+^ **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. |
-| **References to\\ literature**  | to be added                                                                                                                                               ||
+^ **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. |
-| **Lab equipment**               | to be added                                                                                                                                               ||
+^ **Recommended tools and environments** | ROS/ROS2, MOOS-IvP, Autoware, PX4/ArduPilot |
-| **Virtual lab**                 | to be added                                                                                                                                               ||
+^ **Verification and Validation focus** |  |
-| **MOOC course**                 | MOOC Courses hosting for SafeAV, IOT-OPEN.EU Reloaded, and Multiasm grants: http://edu.iot-open.eu/course/index.php?categoryid=3                          ||
+^ **Relevant standards and regulatory frameworks** | ISO 26262, DO-178C, AUTOSAR, JAUS |