====== Module: Human Machine communication (Part 2) ======

^ **Study level** | Master |
^ **ECTS credits** | 1 ECTS |
^ **Study forms** | Hybrid or fully online |
^ **Module aims** | The aim of the module is to introduce safety, validation and societal aspects of human–machine interaction in autonomous systems. The course develops students’ ability to design and evaluate human-centred, explainable and standards-compliant HMI solutions that support usability, trust and safety. |
^ **Pre-requirements** | Basic knowledge of human factors or HMI design principles and interest in system safety. Familiarity with user interface development, AI concepts, ergonomics or safety-related standards is recommended but not mandatory. |
^ **Learning outcomes** | **Knowledge**\\ • Explain safety and reliability concerns in HMI design for autonomous and semi-autonomous systems.\\ • Describe standards and frameworks for HMI validation.\\ • Understand social, ethical, and psychological dimensions influencing public trust in AI-driven systems.\\ • Identify factors affecting cross-cultural and demographic acceptance of automation.\\ **Skills**\\ • Design validation procedures for HMI systems using both experimental and simulation-based testing.\\ • Evaluate user behavior, workload, and situational awareness using quantitative and qualitative methods.\\ • Apply AI tools to simulate user interaction, predict response variability, and analyze safety-related feedback.\\ • Conduct usability assessments and generate compliance reports aligned with HMI safety standards.\\ **Understanding**\\ • Appreciate the ethical importance of transparency, inclusivity, and user autonomy in interface design.\\ • Recognize human limitations and adapt systems to support shared control and human oversight.\\ • Develop awareness of public communication, risk perception, and media framing in acceptance of autonomy. |
^ **Topics** | 1. Human–Machine Interaction Safety:\\    – Human error taxonomy and resilience engineering.\\    – Shared control and human oversight in automated systems.\\ 2. Verification and Validation of HMI:\\    – Testing frameworks, simulation methods, and standards.\\    – Usability metrics: workload, trust, explainability, and accessibility.\\ 3. Public Acceptance and Risk Perception:\\    – Cultural and social factors influencing acceptance of automation.\\    – Role of transparency, explainability, and user trust.\\ 4. AI-Assisted Interaction Evaluation:\\    – Emotion and intent recognition, human-in-the-loop testing.\\    – Adaptive HMIs and predictive user modeling.\\ 5. Standards and Case Studies:\\    – AVSC Best Practices, ISO/SAE frameworks, and real-world HMI validation studies. |
^ **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 theoretical foundations of safety, public trust, and V&V frameworks in HMI.\\ **Lab works** — Implement HMI prototypes and perform usability and safety validation using simulation environments.\\ **Individual assignments** — Evaluate and document HMI validation plans for different user scenarios and safety levels.\\ **Self-learning** — Review literature on human factors, public acceptance, and ethical design in automation. |
^ **AI involvement** | AI tools may assist in user behavior prediction, emotion recognition analysis, and usability simulation. Students must transparently disclose AI usage, validate data integrity, and comply with academic and ethical standards. |
^ **Recommended tools and environments** | Unity, MATLAB, ROS2 |
^ **Verification and Validation focus** |  |
^ **Relevant standards and regulatory frameworks** | ISO 26262, ISO 21448, SAE J3016 |