by arinze.onyebuchi -

Research Matrix

Thesis Topic:
Motion as Communication: Using Micro-Interactions to Help Drivers Understand Automation Mode Changes

Aims ObjectivesMethodsOutcomesOutputs
Understand how drivers currently get confused about automation modes and handovers. Clarify the problem space of mode confusion and existing HMI strategies for communicating mode and takeover.Targeted literature review on mode confusion, takeover studies, and automation HMI guidelines.Clear picture of where current interfaces fail (e.g., unclear state, weak anticipation, bad timing of alerts).Short problem framing section with diagrams of current HMI patterns and failure modes.
Find out what existing research already says about effective feedback and motion in high‑load, time‑critical interfaces. Collect and organize evidence on which feedback types and motion patterns improve comprehension and reaction time.Systematic search, screening, and evidence mapping across automotive, aviation, medical, and UI/motion research. Evidence maps showing which strategies (static, motion, multimodal) work, where, and how strongly they’re supported.Evidence tables and visual maps you can include in the thesis (and reuse in slides).
Translate that evidence into concrete motion patterns and parameters for automation mode changes. Define a compact motion framework for entering automation, exiting automation, and escalating takeover requests.Research‑through‑design: sketching, storyboard flows, prototyping micro‑interactions (e.g., in Figma) guided by the evidence. A small, coherent set of motion patterns with rationale tied back to specific studies and theories.Motion specs (timing, easing, behavior) plus prototype screens showing mode transitions and alerts.
Turn those patterns into reusable motion tokens and a design guide that teams could plug into a design system. Define motion tokens (durations, easing curves, escalation patterns) and describe how to use them in an automotive HMI.Synthesis + systems thinking: abstracting patterns into tokens, writing guidelines, and mapping them into a component library structure.A motion “layer” that can sit inside a design system for vehicle HMIs (or similar products).Motion token set, usage guidelines, and an example component library (e.g., Figma pages or documented components).
Reflect on how this changes the way we think about motion in safety‑critical interaction design. Position motion as a functional communication tool (not just delight) and highlight gaps for future empirical work.Critical discussion that connects your framework back to theory (cognitive load, attention, mode confusion) and identifies missing research. Clear articulation of what we know, what we can recommend with confidence, and what still needs live testing.Discussion + conclusion chapters that wrap up the framework, its limits, and next steps (including ideas for future simulator studies).
by florian.prasse -

Reasearch Matrix Post: Computer Vision-Assisted UI Validation Framework

Validating a User Interface (UI) has traditionally been a bottleneck. While we can easily automate functional tests to see if a button works, determining if that button is aesthetically correct or follows established design principles has always required a human expert. My current research aims to enhance the speed of human expertise by developing a Computer Vision-Assisted UI Validation Framework.

AIM

The primary goal is to design, develop, and evaluate a framework that uses Computer Vision (CV) to objectively assess User Interfaces based on established UI/UX theories. It specifically targets the current problem where UI validation is too resource-intensive and subjective.

OBJECTIVES

Translate Theory: Convert abstract principles like Visual Hierarchy, Gestalt Principles (Proximity/Similarity), and Color Theory into quantifiable metrics.

Build Prototype: Create a tool that can Parse elements (buttons, text), Evaluate them against rules, and Score the results.

Integration: Ensure the framework can be used within existing development workflows, such as CI/CD tools.

METHODS

Research through Design: A four-phase approach involving literature review, dataset construction, implementation, and evaluation.

Technical Stack: Utilizing Python with libraries like OpenCV and PyTorch, specifically employing deep learning models like YOLO for object detection.

Data Sourcing: Building a dataset from award-winning sites (e.g., Awwwards) to compare against “bad” design examples.

OUTCOMES

Objective Validation: An enhancement for subjective human observation to a scalable, automated visual inspection mechanism.

Correlation Findings: Determining the extent to which automated scores can successfully mirror human expert assessments.

Technical Insights: Identifying risks regarding whether high-level concepts like “minimalism” can be truly captured by algorithms.

OUTPUTS

The Workpiece: A software application or library that takes a URL or screenshot and generates a structured report.

Visual Overlays: Heatmaps or overlays on the UI that point out exactly where rules (like alignment or contrast) are violated.

Master’s Thesis: A formal document covering the translation gap between UX and Computer Vision.