Human-Centered Research for Future In-Vehicle Experiences
Driver interaction, usability, and decision-making in next-generation vehicle systems
Role
HCI Researcher
Client
General Motors
Responsibilities
User Research & Analysis, Usability Testing, Behavioral Observation, Affinity Mapping, Pattern Synthesis, Interface Clarity Recommendations
Methods
Contextual inquiry, Task-based usability testing, Behavioral pattern analysis
Project Brief
As vehicles become increasingly software-driven, the driver’s interaction with in-vehicle systems grows more complex. Features such as energy monitoring, drive-mode selection, and vehicle diagnostics introduce additional cognitive demands while driving.
Our challenge was to understand:
- How drivers interpret vehicle information systems
- Where interaction friction or cognitive overload occurs
- How interface design influences driver awareness and decision-making
The goal was to generate research insights that could inform safer and more intuitive vehicle interfaces.
Research Approach
To understand driver behavior in context, I conducted a human-centered research process combining qualitative and behavioral data.
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1
Contextual Inquiry
Observed drivers interacting with the vehicle interface during controlled testing sessions. Focus areas included information scanning patterns, response time to system feedback, and misinterpretation of vehicle signals or alerts.
This helped identify moments of hesitation, confusion, and decision delays in real driving scenarios.
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2
Usability Testing
Designed task-based usability sessions where participants performed common interactions: checking energy usage, adjusting drive settings, interpreting system alerts. Metrics collected included task completion time, error frequency, and navigation patterns across the interface.
This allowed us to identify specific UI elements that increased cognitive load or slowed interaction.
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3
Behavioral Pattern Analysis
After collecting interaction data, I analyzed patterns across participants to detect recurring usability issues. Key synthesis methods included affinity mapping of user behaviors, error clustering across tasks, and identifying interaction bottlenecks.
This process helped move from individual observations to systemic design insights.
Challenges that manufacturing & robotics leaders face
1. Information Hierarchy Impacts Driver Attention
Drivers struggled when multiple vehicle metrics were presented simultaneously. Interfaces with unclear hierarchy required longer glance durations, increasing cognitive load.
Insight: Critical driving information must be visually prioritized and separated from secondary data.
2. Terminology Influences Driver Decision-Making
Certain system labels and technical terms were interpreted differently by drivers, leading to hesitation during task execution.
Insight: Language within vehicle interfaces should prioritize action clarity over technical accuracy.
3. Feedback Loops Need Stronger Visibility
Drivers often completed actions without clear confirmation from the system, creating uncertainty.
Insight: Immediate and perceptible feedback reduces driver doubt and interaction repetition.
Impact
The research produced actionable design recommendations for improving in-vehicle interface clarity and driver interaction efficiency.
- Identifying interaction friction points within the vehicle system
- Providing research-backed recommendations for interface hierarchy
- Highlighting cognitive load considerations for driver-facing software
These insights informed design considerations for future vehicle HMI systems, emphasizing safety, clarity, and usability.
Skills Demonstrated
HCI Research Methods
Contextual inquiry, Usability testing, Behavioral observation
Research Analysis
Affinity mapping, Pattern synthesis, Usability metrics analysis
Human-Centered Design
Cognitive load evaluation, Interaction flow analysis, Interface clarity recommendations