Google Maps Baseline Metrics

01.

Role and

Responsibilities

02.

Project Summary

03.

The Challenge

04.

The Solution

05.

Results

Role and Responsibilities

Company: Google Maps

Project date: March 2022

Role: Assistant UXR

Responsibilities:

Co-moderated (hardware & tech set up, data intake, note taking)
Quantitative & qualitative data cleaning and analysis (eye tracking, ratings, user interview)
Co-wrote/presented report deck
Created "How to" on-road methodology guide to be replicable.

Project Summary

Google maps driving and navigation UXR's worked together to design, conduct, and analysis on-road user studies with the goal to create a replicable on-road methodology and collect baseline metrics of the drivers experience when following a route.

Goals:

Establish a reliable and reusable on-road testing methodology.
Establish baseline glance metrics on common maneuvers.
Explore, analysis and evaluate what drivers reference (glances), how stress (heart rate), and perceived workload (NASA-TLX) co-exist in a driving journey.
Identify opportunities and improvements as maps launches new experiences.

Method:

90 minute on-road driving evaluation with pre-approved routes.
22 participants
Tobi eye tracking glasses
Fitbit heart rate tracking
Rating questionnaires (NASA-TLX)
Task success & observations
User interviews

The Challenge

As Google maps experience is evolving, the UXR driving and navigation team wanted to establish baseline metrics to assess how these changes affect drivers safety and overall "follow the route" experience. We were challenged with defining and assessing baseline metrics that objectively measure a drivers experience as it relates to safety. However, how do we define driver safety objectively when safely "following the route" can be interpreted differently between drivers?

The Solution

Merge quantitative and qualitative data to establish a baseline metric that accesses the drivers "follow the route" experience from an behavioral and subjective perspective.
Established baseline metrics, using a mixed methods approach that provides quantitative (glances, task success rate, heart rate, etc.) and qualitative understanding of drivers behaviors.
Access baseline metrics based on characteristics of route which would include road classification (residential, city, highway) and navigation tasks (orientation, turns, lane crossing, merges, etc.)

The Results

Deepen our understanding of drivers top pain points while following navigation, highlighting opportunities for improvement and helping us prioritize research needs.
Positive correlation between drivers behavioral performance (glances, task success, heart rate) and subjectives rating. More glances, higher heart rate and tasks not successfully completed = higher perceived workload and lower satisfaction.