Automotive HMI & UX Constraints in Retrofit Vehicles (1st Offering)
Presenter: Farzaneh Kazemzadeh
Date: Tuesday, February 10th, 2026
Time: P1-O1 at 10:00 AM, P1-O2 at 12:00 PM
Location: 4th Floor - 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
Abstract:
Retrofit vehicles are older vehicles that have been partially upgraded with modern digital systems, which creates unique challenges for interface design. Unlike newly manufactured cars, retrofit systems operate under strict limitations such as small displays, limited processing power, legacy hardware, and constrained input methods. In these conditions, poor interface decisions can easily confuse drivers, reduce attention, and increase the risk of errors. This workshop focuses on understanding the practical UI/UX constraints present in retrofit vehicles and how these constraints should shape design decisions. Participants will explore common design mistakes in automotive interfaces, learn why adding more information is often harmful, and understand how to balance retro aesthetics with usability and safety. By the end of the workshop, attendees will gain a clearer mindset for designing interfaces that are functional, readable, and appropriate for real-world retrofit automotive environments.
Workshop Outline:
- Understanding what makes retrofit vehicles different from newly manufactured cars
- Key interface limitations in retrofit automotive systems and why they matter
- Common UI/UX mistakes in legacy vehicle interfaces
- Why showing more information can reduce safety and usability
- Balancing retro visual identity with clarity and driver focus
- Evaluating interface decisions through real-world automotive scenarios
Prerequisites:
No prior experience with design tools is required. A general interest in vehicles, digital interfaces, or user experience is sufficient. This workshop is suitable for students from diverse technical and design backgrounds.
Biography:
Farzaneh Kazemzadeh is a PhD student in Computer Science at the University of Windsor. Her research focuses on trustworthy AI, particularly on privacy-preserving machine learning, with applications in genomics and social networks. Her current work explores memorization and privacy risks in large language models.
1st Offering (10AM):
2nd Offering (12PM):