| 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 |