Ignacio Alvarez

Ignacio Alvarez

Professor and Principal Engineer in Intelligent Systems

Teaching

2025

Study design and implementation of UX tests: Emphatic Car

User Experience Design Bachelor of Science, Technische Hochschule Ingolstadt, Computer Science, 2025

Description

This course is designed to prepare students for their final thesis. It provides fundamental knowledge to develop a study design based on a problem definition, conduct the study, evaluate it, and interpret the results. Thematic clusters in the area of ​​human-computer interaction (e.g., productivity, automated driving, sports, and digitalization) are offered, from which student groups can choose and propose a specific topic. This topic will be developed iteratively in close professional coordination with the respective supervising lecturers. • Basics of user studies (possible applications, definition of research hypothesis) • Study design (dependent/independent variables, laboratory vs. field studies, within-groups/between-groups design) • Planning of experiments (different methods, “fidelity” of an experiment, software/hardware prototypes, Wizard of Oz studies, qualitative surveys/quantitative measurements, ethical aspects, role of an institutional review board (IRB)) • Study implementation (preparation, briefing/debriefing, finding and inviting test subjects, determining group size, learning effects, measuring variables/data collection) • Qualitative data analysis (content analysis, evaluation with MAXQDA/NVIVO, preparation and presentation of results, revision/fine-tuning in Illustrator) • Quantitative data analysis (reflection on the research hypothesis, use of SPSS for statistical evaluations, parametric/nonparametric statistics, correct choice of method, presentation and interpretation of results) • Written/oral presentation of the results (preparation of results, peer review process, final conference-style presentation)

Study design and implementation of UX tests: AV harrassment

User Experience Design Bachelor of Science, Technische Hochschule Ingolstadt, Computer Science, 2025

Description

This course is designed to prepare students for their final thesis. It provides fundamental knowledge to develop a study design based on a problem definition, conduct the study, evaluate it, and interpret the results. Thematic clusters in the area of ​​human-computer interaction (e.g., productivity, automated driving, sports, and digitalization) are offered, from which student groups can choose and propose a specific topic. This topic will be developed iteratively in close professional coordination with the respective supervising lecturers. • Basics of user studies (possible applications, definition of research hypothesis) • Study design (dependent/independent variables, laboratory vs. field studies, within-groups/between-groups design) • Planning of experiments (different methods, “fidelity” of an experiment, software/hardware prototypes, Wizard of Oz studies, qualitative surveys/quantitative measurements, ethical aspects, role of an institutional review board (IRB)) • Study implementation (preparation, briefing/debriefing, finding and inviting test subjects, determining group size, learning effects, measuring variables/data collection) • Qualitative data analysis (content analysis, evaluation with MAXQDA/NVIVO, preparation and presentation of results, revision/fine-tuning in Illustrator) • Quantitative data analysis (reflection on the research hypothesis, use of SPSS for statistical evaluations, parametric/nonparametric statistics, correct choice of method, presentation and interpretation of results) • Written/oral presentation of the results (preparation of results, peer review process, final conference-style presentation)

Project Artificial Intelligence: LLM-based Agent for Driver Sleepiness Detection and Mitigation in Automotive Systems

AI Engineering of Autonomous Systems Master of Science, Technische Hochschule Ingolstadt, Computer Engineering, 2025

Description

Ensuring driver alertness is a cornerstone of automotive safety, and Large Language Models (LLMs) offer a unique opportunity to create intelligent systems capable of detecting and mitigating sleepiness. By integrating multimodal inputs such as audio cues, video streams, and driving context signals, LLMs can process complex, real-time data to assess driver state and trigger appropriate actions to maintain alertness. In this project, students will design and prototype an automotive agent powered by LLMs to detect and respond to driver sleepiness. The system will utilize multimodal inputs, such as facial expressions, voice tone, and driving behavior, to compute a sleepiness likelihood metric. Based on the metric and additional contextual awareness signals (e.g., time of day, driving duration), the agent will propose tailored interventions, such as adjusting cabin temperature, suggesting a rest stop, or initiating engaging conversations. Students will evaluate the system’s usability, effectiveness, and user satisfaction, exploring the interplay between AI decision-making and driver interaction.

Project Natural User Interfaces: Enhancing Autonomous Vehicle Safety Awareness through Gamification

User Experience Design Master of Science, Technische Hochschule Ingolstadt, Computer Science, 2025

Description

Educating vehicle passengers and drivers about safety in autonomous vehicles (AVs) can be a challenge, especially when technical concepts like the Responsibility Sensitive Safety (RSS) model are involved. Gamification offers an innovative way to enhance user engagement and increase awareness of AV safety principles through the more natural interfaces of games by transforming complex concepts into interactive and enjoyable experiences. Integrating a safety model like RSS into a game can provide users with real-time feedback on safe driving practices and decision-making in various scenarios, as well as improve overall situational awareness during non-driving tasks.