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| en:safeav:curriculum:ctrl-m [2025/10/22 12:09] – [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 provide students with advanced theoretical and practical knowledge in the validation and verification of control, planning, and decision-making systems used in autonomous platforms. The module explores simulation-based testing, formal verification, and model-checking techniques that ensure the safety and stability of AI-enhanced controllers. Students will learn to integrate hybrid simulation frameworks, formal reasoning tools, and optimization algorithms to assess real-world readiness of control architectures. Emphasis is placed on quantifiable safety guarantees, robustness against uncertainty, and compliance with international standards (ISO 26262, ISO 21448, and IEEE 2846). || | ^ **Study forms** | Hybrid or fully online | |
| | **Pre-requirements** | Strong background in control theory, optimization, and planning algorithms. Familiarity with programming (Python, C++, MATLAB), model-based design tools (Simulink, ROS 2), and AI decision-making frameworks. Basic understanding of formal methods, hybrid systems, and system modeling. Prior exposure to simulation environments or real-time control applications is recommended. || | ^ **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** | 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 ISO 26262, ISO 21448 (SOTIF), and IEEE 2846 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 (MATLAB/Simulink, ROS 2, CARLA).\\ • 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/Attitudes:\\ • 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. || | ^ **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 ** | 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. || | ^ **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** | Lectures: 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, 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. || | ^ **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** | Yes — 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. | | | ^ **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** | 1. Rajamani, R. (2012). Vehicle Dynamics and Control (2nd ed.). Springer.\\ 2. Baier, C., & Katoen, J.-P. (2018). Principles of Model Checking. MIT Press.\\ 3. Koopman, P., & Widen, J. (2023). The AI Driver: Defining Human-Equivalent Safety for Automated Vehicles. IEEE Transactions on Intelligent Vehicles.\\ 4. ISO 21448 (2022). Road Vehicles – Safety Of The Intended Functionality (SOTIF).\\ 5. ISO 26262 (2018). Road Vehicles – Functional Safety.\\ 6. IEEE 2846 (2022). Assumptions for Models in Safety-Related Automated Vehicle Behavior.\\ 7. Althoff, M., et al. (2021). Formal Verification of Autonomous Systems: State of the Art and Future Directions. IEEE Access.\\ 8. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. || | ^ **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** | Yes || | ^ **Recommended tools and environments** | MATLAB/Simulink, ROS2, CARLA, UPPAAL, SPIN, or CBMC | |
| | **Virtual lab** | Yes || | ^ **Verification and Validation focus** | | |
| | **MOOC course** | Suggested MOOC: 'Formal Methods for Autonomous Systems' (edX, University of York) or 'Verification and Validation of AI Systems' (Coursera, Stanford University). || | ^ **Relevant standards and regulatory frameworks** | ISO 26262, ISO 21448 (SOTIF), and IEEE 2846, ASAM OpenSCENARIO | |
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