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| en:safeav:curriculum:hmc-b [2025/10/20 14:54] – [Table] larisas | en:safeav:curriculum:hmc-b [2025/11/05 09:09] (current) – airi |
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| ====== Module: Human Machine communication (Part 1) ====== | ====== Module: Human Machine communication (Part 1) ====== |
| | **Study level** | Bachelor || | |
| | **ECTS credits** | 1 ECTS || | ^ **Study level** | Bachelor | |
| | **Study forms** | Hybrid or fully online || | ^ **ECTS credits** | 1 ECTS | |
| | **Module aims** | To provide students with a comprehensive understanding of Human–Machine Interaction (HMI) and communication systems in autonomous vehicles. The module explores how autonomous systems perceive, interpret, and communicate with humans—both passengers and external participants such as pedestrians and other drivers. It introduces the concept of the 'Language of Driving' as a framework for structuring interaction, focusing on cognitive, cultural, and social aspects of communication. Students will learn how AI-driven interfaces, multimodal communication, and human-centered design contribute to safety, trust, and usability in human–vehicle collaboration. || | ^ **Study forms** | Hybrid or fully online | |
| | **Pre-requirements** | Basic knowledge of human factors, cognitive science, and control systems. Familiarity with psychology or user interface design concepts is advantageous. Programming experience (e.g., Python, C++, or similar) and understanding of embedded or AI-based systems are beneficial for implementing and testing interactive communication modules. || | ^ **Module aims** | The aim of the module is to introduce human–machine interaction and communication concepts for autonomous vehicles. The course develops students’ understanding of how autonomous systems perceive, interpret and communicate with humans using AI-driven, multimodal and human-centred interfaces that support safety, trust and usability. | |
| | **Learning outcomes** | Knowledge:\\ • Explain the principles of HMI and multimodal communication in autonomous systems.\\ • Describe human perceptual and cognitive models relevant to interaction with machines.\\ • Understand the cultural, ethical, and social dimensions influencing communication design.\\ • Recognize standards and best practices in safety-critical HMI (e.g., AVSC, SAE ITC).\\ Skills:\\ • Design and prototype human–machine interfaces that enhance trust and situational awareness.\\ • Evaluate user experience (UX) using qualitative and quantitative assessment techniques.\\ • Integrate AI-based dialogue, gesture, and visual communication components within simulation environments.\\ Understanding/Attitudes:\\ • Appreciate the need for transparency, inclusivity, and cultural sensitivity in AI communication.\\ • Critically assess the ethical implications of human–autonomy collaboration.\\ • Foster responsible design thinking in developing communication frameworks for AVs. || | ^ **Pre-requirements** | Basic knowledge of human factors or cognitive science and interest in user-centred design. Familiarity with control systems and programming and AI-based or embedded systems is recommended but not mandatory. | |
| | ** Topics ** | 1. Introduction to Human–Machine Interaction in Autonomous Vehicles:\\ – Role of HMI in safety, trust, and usability.\\ – Transition from driver-operated to fully autonomous systems.\\ 2. Human Perception and Cognition:\\ – Human sensory systems, attention, and response time.\\ – Comparing human and AI perception models.\\ 3. Communication Modalities:\\ – Visual, auditory, and haptic feedback; external vehicle communication signals.\\ – Pedestrian and passenger interaction mechanisms.\\ 4. The Language of Driving (LoD):\\ – Concept and design of shared communication languages between humans and AVs.\\ – Cultural, geographical, and environmental factors in interaction design.\\ 5. AI in Communication:\\ – Role of conventional and LLM-based AI in HMI design and dialogue management.\\ 6. Standards and Case Studies:\\ – AVSC best practices, SAE standards, and real-world HMI deployments. || | ^ **Learning outcomes** | **Knowledge**\\ • Explain the principles of HMI and multimodal communication in autonomous systems.\\ • Describe human perceptual and cognitive models relevant to interaction with machines.\\ • Understand the cultural, ethical, and social dimensions influencing communication design.\\ • Recognize standards and best practices in safety-critical HMI.\\ **Skills**\\ • Design and prototype human–machine interfaces that enhance trust and situational awareness.\\ • Evaluate user experience using qualitative and quantitative assessment techniques.\\ • Integrate AI-based dialogue, gesture, and visual communication components within simulation environments.\\ **Understanding**\\ • Appreciate the need for transparency, inclusivity, and cultural sensitivity in AI communication.\\ • Critically assess the ethical implications of human–autonomy collaboration.\\ • Foster responsible design thinking in developing communication frameworks for AVs. | |
| | **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. Introduction to Human–Machine Interaction in Autonomous Vehicles:\\ – Role of HMI in safety, trust, and usability.\\ – Transition from driver-operated to fully autonomous systems.\\ 2. Human Perception and Cognition:\\ – Human sensory systems, attention, and response time.\\ – Comparing human and AI perception models.\\ 3. Communication Modalities:\\ – Visual, auditory, and haptic feedback; external vehicle communication signals.\\ – Pedestrian and passenger interaction mechanisms.\\ 4. The Language of Driving:\\ – Concept and design of shared communication languages between humans and AVs.\\ – Cultural, geographical, and environmental factors in interaction design.\\ 5. AI in Communication:\\ – Role of conventional and LLM-based AI in HMI design and dialogue management.\\ 6. Standards and Case Studies:\\ – AVSC best practices, SAE standards, and real-world HMI deployments. | |
| | **Learning methods** | Lectures: Cover theories of human perception, cognitive ergonomics, and communication in autonomous systems.\\ Lab works: Practical development of HMI prototypes (e.g., dashboard simulation, pedestrian signaling models) using Python or Unity.\\ Individual assignments: Analytical essays or UX evaluations of existing HMI systems.\\ Self-learning: Review of international standards and exploration of open-source HMI datasets and design tools. || | ^ **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 design conversational interfaces, simulate interaction scenarios, and analyze user feedback. Students must disclose AI assistance transparently and validate all outputs to maintain research and ethical standards. | | | ^ **Learning methods** | **Lecture** — Cover theories of human perception, cognitive ergonomics, and communication in autonomous systems.\\ **Lab works** — Practical development of HMI prototypes (e.g., dashboard simulation, pedestrian signaling models) using Python or Unity.\\ **Individual assignments** — Analytical essays or UX evaluations of existing HMI systems.\\ **Self-learning** — Review of international standards and exploration of open-source HMI datasets and design tools. | |
| | **References to\\ literature** | 1. Norman, D. A. (2013). The Design of Everyday Things (Revised and Expanded Edition). Basic Books.\\ 2. Hollnagel, E. (2017). Safety-II in Practice: Developing the Resilience Potentials. Routledge.\\ 3. Endsley, M. R. (2018). Situation Awareness in Automation: Designing for Shared Understanding. Human Factors, 60(4).\\ 4. AVSC Best Practice for Passenger-Initiated Emergency Trip Interruption (2023). SAE ITC/AVSC.\\ 5. Sheridan, T. B. (2016). Human–Robot Interaction: Status and Challenges. Human Factors, 58(4).\\ 6. Goodrich, M. A., & Schultz, A. C. (2007). Human–Robot Interaction: A Survey. Foundations and Trends in Human–Computer Interaction.\\ 7. OECD/ITF (2022). Safe Human–Machine Interaction for Autonomous Vehicles. OECD Publishing. || | ^ **AI involvement** | AI tools may be used to design conversational interfaces, simulate interaction scenarios, and analyze user feedback. Students must disclose AI assistance transparently and validate all outputs to maintain research and ethical standards. | |
| | **Lab equipment** | Yes || | ^ **Recommended tools and environments** | Unity, MATLAB, ROS2 | |
| | **Virtual lab** | Yes || | ^ **Verification and Validation focus** | | |
| | **MOOC course** | Suggested MOOC: 'Human–Computer Interaction' (University of California, San Diego – Coursera) or 'AI for Human–Robot Interaction' (MITx – edX). || | ^ **Relevant standards and regulatory frameworks** | AVSC, SAE ITC | |
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