Android Auto User Experience: Eye-Tracking and Usability Study

A comprehensive UX study evaluating Android Auto's usability and driver distraction through eye-tracking and HCI methodologies.

UX ResearchEye-TrackingHCI
2 min read
Android Auto User Experience: Eye-Tracking and Usability Study

Situation:

With the rise of complex In-Vehicle Information Systems (IVIS) like Android Auto, driver distraction has become a primary cause of road accidents. There was a critical need to evaluate how these digital interfaces balance functionality with driver safety, specifically regarding cognitive workload and off-road fixations.

Task:

Directed a user study to evaluate the usability of Android Auto’s core features—navigation, music, and messaging—and to investigate how different interaction modalities (touch vs. voice) and driver experience levels impact distraction.

Action:

  • Designed and executed a mixed-methods experiment involving eight participants in a controlled, stationary in-car environment with a simulated highway driving scenario.
  • Developed and deployed custom eye-tracking software to collect quantitative data on glance frequency, timestamped events, and duration of interactions with the vehicle’s central console.
  • Implemented a rigorous HCI methodology utilizing the Single Ease Question (SEQ) for task-specific assessment and the Usability Metric for User Experience (UMUX) to quantify overall system satisfaction and requirements fulfillment.
  • Conducted qualitative analysis through open-ended post-test questionnaires and supervisor observations to identify specific pain points in UI components, such as keyboard autocompletion and voice assistant responsiveness.

Result:

  • Quantified system usability with a UMUX score of 66.15/100, identifying a critical friction point in “time spent correcting errors” (sub-score of 52.08), particularly regarding on-screen keyboard typing for navigation.
  • Identified that while voice interfaces improved perceived safety and speed in messaging, system latency and poor autocomplete algorithms significantly increased cognitive workload and distraction.
  • Provided actionable design recommendations to reduce user intervention by improving system accuracy in speech recognition and autocompletion to enhance road safety.
Subject point of view
Subject Point of View
Driving simulation
Driving simulation
Experimenter dashboard
Experimenter Dashboard