Workshop Title “Designing the Driver's Eye: Sports Car Dashboard UI/UX”
Presenter: Reem Al-Saidi
Date: 27 Jan 2026
Time: 1st offering 9-10 AM 2nd offering 11 AM-12 PM
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
Abstract:
The workshop "Designing the Driver's Eye: Sports Car Dashboard UI/UX" covers the principles and safety requirements essential for creating effective automotive instrument clusters. Unlike typical UI/UX, automotive interface design operates in a life-or-death environment where every decision impacts driver safety.
Participants will explore the science behind dashboard design, including the critical 1.5-second rule from NHTSA research, visual hierarchy systems that prioritize speed, RPM, and warnings, and the semantic color language universal to automotive interfaces. Special attention is given to sports car UI demands — where drivers operate at extreme speeds and rely heavily on performance metrics.
Through case studies of Porsche, Ferrari, and McLaren, participants will understand proven approaches to balancing emotional design with functional safety, preparing them to create dashboard concepts for the C2 Sports Car Challenge.
Workshop Outline:
· The stakes of automotive UI — Why dashboard design is life-or-death
· The 1.5-second rule and visual hierarchy — Glance time research and the three-tier system
· Speedometer, tachometer, and warning systems — Designing critical dashboard elements
· Color theory and typography — Semantic colors and high-speed readability
· Day/Night modes and drive mode transformations — Adaptive interface design
· Case studies — Lessons from Porsche, Ferrari, and McLaren
· C2 Sports Car Challenge brief — Requirements and evaluation criteria
Prerequisites:
- Basic familiarity with design software (Photoshop, Illustrator, Sketch, or similar — helpful but not required)
- Interest in automotive UI/UX design and the C2 Sports Car Challenge
- Familiarity with Figma (preferably attend workshop 1)
Biography:
Reem Al-Saidi is a PhD student in Computer Science at the University of Windsor. Her research focuses on privacy-preserving machine learning, with a particular emphasis on large language models (LLMs) for health and genomic data, utilizing a cloud environment. Her current work explores secure data sharing and publishing through deep learning–based synthetic data generation.
Registration:
9AM Offering:
11AM Offering: