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| en:safeav:curriculum:ctrl-m [2025/10/21 08:35] – [Table] larisas | en:safeav:curriculum:ctrl-m [2025/11/05 09:19] (current) – airi |
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| ====== Module: Control, Planning, and Decision-Making (Part 2) ====== | ====== Module: Control, Planning, and Decision-Making (Part 2) ====== |
| | **Study level** | Master || | |
| | **ECTS credits** | 1 ECTS || | ^ **Study level** | Master | |
| | **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 validation and verification methods for control, planning and decision-making in autonomous systems. The course develops students’ ability to design, execute and interpret simulation-based and formal testing workflows that assess safety, robustness and standards compliance of autonomy controllers. | |
| | **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 control theory, optimisation and planning algorithms, as well as programming skills or MATLAB. Familiarity with model-based design tools, AI decision-making frameworks or simulation and real-time control environments 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 simulation-based and formal validation approaches for control and planning systems.\\ • Describe the use of model-checking, reachability analysis, and verification frameworks in autonomous systems.\\ • Understand standards relevant to control and decision-making validation.\\ • Discuss trade-offs between simulation fidelity, computational efficiency, and real-time constraints.\\ **Skills**\\ • Develop and validate control and planning algorithms in simulation environments.\\ • Apply formal verification tools to analyze safety and correctness properties.\\ • Design hybrid validation workflows combining Monte Carlo simulation and symbolic reasoning.\\ • Evaluate algorithm robustness and decision safety under stochastic and adversarial conditions.\\ **Understanding**\\ • Appreciate the role of rigorous validation in certifying autonomous behaviors and AI-based decision-making.\\ • Recognize limitations of current simulation and formal verification tools in high-dimensional, data-driven systems.\\ • Adopt ethical, transparent, and standards-compliant practices in the assurance of autonomy. | |
| | **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. Validation of Control and Planning Systems:\\ – System-level validation frameworks and verification-driven design.\\ – Simulation fidelity, corner-case testing, and scenario coverage.\\ 2. Simulation Environments and Tools:\\ – SIL/HIL setups, Monte Carlo analysis, and statistical validation.\\ – Multi-domain co-simulation for cyber-physical systems.\\ 3. Formal Verification and Model Checking:\\ – Safety property specification and temporal logic.\\ – Reachability analysis, invariant verification, and constraint solving.\\ 4. Hybrid and Nonlinear Systems:\\ – Modeling hybrid automata and nonlinear control loops.\\ – Formal abstraction and conservative over-approximation techniques.\\ 5. Standards and Safety Frameworks:\\ – ISO 26262, ISO 21448, IEEE 2846, and ASAM OpenSCENARIO for validation.\\ 6. Case Studies:\\ – Autonomous driving, UAV flight control, and robotic path planning validation. | |
| | **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** — Cover theory and methodologies for simulation-based and formal validation of control and planning systems.\\ **Lab works** — Implement and test controllers in virtual and hybrid environments (ROS2, MATLAB, CARLA, Scenic, CommonRoad, UPPAAL).\\ **Individual assignments** — Develop validation pipelines, perform reachability analysis, and document results.\\ **Self-learning** — Study research papers and international standards on autonomy verification and formal safety assurance. | |
| | **References to\\ literature** | to be added || | ^ **AI involvement** | AI tools may be used to automate scenario generation, identify unsafe trajectories, and optimize validation coverage. Students must validate AI-assisted outcomes, ensure reproducibility, and cite AI involvement transparently in deliverables. | |
| | **Lab equipment** | to be added || | ^ **Recommended tools and environments** | MATLAB/Simulink, ROS2, CARLA, UPPAAL, SPIN, or CBMC | |
| | **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), and IEEE 2846, ASAM OpenSCENARIO | |
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