| 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 |