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--- en:safeav:curriculum:ctrl-m [2025/11/04 16:59] – raivo.sell
+++ en:safeav:curriculum:ctrl-m [2025/11/05 11:19] (current) – airi
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 ^ **Study forms** | Hybrid or fully online |
 ^ **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. |
-^ **Pre-requirements** | Basic knowledge of control theory, optimisation and planning algorithms, as well as programming skills in Python, C++ or MATLAB. Familiarity with model-based design tools, AI decision-making frameworks or simulation and real-time control environments is recommended but not mandatory. |
+^ **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. |
-^ **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 (UPPAAL, SPIN, or CBMC) 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. |
+^ **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. |
-^ **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 (LTL, CTL).\\    – 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. |
+^ **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. |
 ^ **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** — 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 (ROS 2, 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. |
+^ **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. |
 ^ **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. |
-^ **Recommended tools and environments** | MATLAB/Simulink, ROS 2, CARLA, UPPAAL, SPIN, or CBMC |
+^ **Recommended tools and environments** | MATLAB/Simulink, ROS2, CARLA, UPPAAL, SPIN, or CBMC |
 ^ **Verification and Validation focus** |  |
 ^ **Relevant standards and regulatory frameworks** | ISO 26262, ISO 21448 (SOTIF), and IEEE 2846, ASAM OpenSCENARIO |