====== Module: Control, Planning, and Decision-Making (Part 1) ======

^ **Study level** | Bachelor |
^ **ECTS credits** | 1 ECTS |
^ **Study forms** | Hybrid or fully online |
^ **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. |
^ **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. |
^ **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. |
^ **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. |
^ **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** — 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. |
^ **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. |
^ **Recommended tools and environments** | FSM, Behavior Trees, A*, RRT, MPC |
^ **Verification and Validation focus** |  |
^ **Relevant standards and regulatory frameworks** | ISO 26262, ISO 21448 (SOTIF), SAE J3016 |