Designing Automotive HMIs with Human Attention Constraints in Retrofit Vehicles
Presenter: Farzaneh Kazemzadeh
Date: Friday, February 13th, 2026
Time: P2-O1 at 10:00 AM, P2-O2 at 12:00 PM
Location: 4th Floor - 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
Abstract:
Designing interfaces for retrofit vehicles is not only a technical challenge, but also a human attention problem. Drivers operate under strict attention limits, especially when interacting with digital dashboards while driving. In retrofit vehicles, these limits are amplified due to simple displays, limited visual space, and the absence of advanced driver assistance systems.
This workshop focuses specifically on how to design automotive HMIs by prioritizing human attention rather than system capabilities. Participants will learn how poor color choices, dense layouts, and unprioritized indicators increase cognitive load and distraction. Through visual examples of real dashboard interfaces, the session demonstrates how speed, RPM, warnings, and secondary information should be visually structured, simplified, or suppressed depending on driving context.
By the end of the workshop, participants will be able to critically evaluate dashboard designs, identify attention-related design flaws, and apply practical guidelines for color usage, layout hierarchy, and information filtering that are suitable for retrofit vehicles.
Workshop Outline:
- Cognitive load and visual distraction in dashboard design
- How color, contrast, and layout affect driver focus
- Common attention-related problems in speedometers, RPM displays, and alerts
- Case study: analyzing a sample dashboard interface
- Practical guidelines for designing attention-aware dashboards in retrofit vehicles
Prerequisites:
No prior experience with automotive interface design or human factors is required. A general interest in vehicles, user interfaces, or human-centered design is sufficient. This workshop is suitable for students from both technical and non-technical 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.
Registration