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--- en:safeav:curriculum:ctrl-b [2025/10/20 14:25] – [Table] larisas
+++ en:safeav:curriculum:ctrl-b [2025/11/05 09:07] (current) – airi
@@ Line 1: / Line 1: @@
 ====== Module: Control, Planning, and Decision-Making (Part 1) ======
-| **Study level**                 | Bachelor                                                                                                                                                  ||
-| **ECTS credits**                | 1 ECTS                                                                                                                                                    ||
+^ **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 control and planning methods for autonomous systems. The course develops students’ ability to design and analyse feedback control, motion planning and decision-making algorithms that generate safe and reliable vehicle behaviour in dynamic environments, using both classical and modern AI-based approaches. |
-| **Learning outcomes**           | After completing this module (for every topic listed below), the student:\\  - knows x\\  - knows y\\  - understands z\\  - can w                         ||
+^ **Pre-requirements** | Basic knowledge of linear algebra, differential equations and control theory, as well as programming skills. Familiarity with system dynamics, robotics or numerical tools (e.g. MATLAB/Simulink) is recommended but not mandatory. |
-| ** Topics **                    | __Topic AV1 __ (1 ECTS) \\ \\ __Topic AV2 __ (2 ECTS)) \\ \\ __Topic AV3 __ (2 ECTS)\\ \\ __Topic AV4 __ (1 ECTS) \\                                      ||
+^ **Learning outcomes** | **Knowledge**\\ • Explain classical control principles and their application to vehicle dynamics.\\ • Describe AI-based control methods, including reinforcement learning and neural network controllers.\\ • Understand motion planning and behavioral algorithms\\ • Discuss safety verification, validation, and certification issues for autonomous control systems.\\ **Skills**\\ • Design, simulate, and tune classical controllers for trajectory tracking and stabilization.\\ • Implement basic reinforcement learning or hybrid control strategies in simulation environments.\\ • Develop motion planning pipelines integrating perception, planning, and control layers.\\ **Understanding**\\ • Recognize trade-offs between transparency, performance, and adaptability in control architectures.\\ • Evaluate robustness, explainability, and ethical implications in AI-driven control.\\ • Appreciate interdisciplinary approaches to achieve safe and reliable autonomous operation. |
-| **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. Classical Control Strategies:\\    – Feedback control fundamentals, PID design and tuning, LQR, Sliding Mode Control.\\    – Model Predictive Control and real-time optimization.\\ 2. AI-Based Control Strategies:\\    – Reinforcement learning for control, supervised imitation learning.\\    – Neural network controllers and hybrid architectures.\\ 3. Integration and Safety:\\    – Verification, validation, and certification of control systems.\\    – Robustness, interpretability, and failure handling.\\ 4. Motion Planning and Behavioral Algorithms:\\    – FSMs, Behavior Trees, and rule-based systems.\\    – Planning methods: A*, D*, RRT, RRT*, and MPC-based trajectory generation.\\    – Predictive and optimization-based planning for dynamic environments.\\ 5. Future Trends:\\    – Explainable AI control, safe RL, and human-like behavioral models. |
-| **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** — Introduce theoretical and mathematical foundations of classical and AI-based control strategies.\\ **Lab works** — Implement and compare controllers (PID, LQR, RL) and motion planners (A*, RRT) using simulation tools such as low-fidelity planning simulators, or MATLAB/Simulink.\\ **Individual assignments** — Design a control or planning pipeline and evaluate safety/performance trade-offs.\\ **Self-learning** — Independent exploration of open-source control frameworks and reading of selected research literature. |
-| **References to\\ literature**  | to be added                                                                                                                                               ||
+^ **AI involvement** | Students may use AI tools to generate code templates, optimize control parameters, or analyze planning performance. All AI-assisted work must be reviewed, validated, and cited properly in accordance with academic integrity standards. |
-| **Lab equipment**               | to be added                                                                                                                                               ||
+^ **Recommended tools and environments** | FSM, Behavior Trees, A*, RRT, MPC |
-| **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, ISO 21448 (SOTIF), SAE J3016 |

en/safeav/curriculum/ctrl-b.1760970327.txt.gz · Last modified: 2025/10/20 14:25 by larisas