Sprint 3: Feeling Inclined to Pretotype

Two weeks of digging deep into exploratory, generative research

Interviews & Observations

Over the past two weeks, our team has been hard at work learning all about transportation, accessibility, artificial intelligence, and human-robot interaction. We cast a wide net with our research methods and completed

• 13 informational interviews with experts and stakeholders in our focus areas
• Observation of a meeting of the City-County Task Force on Disabilities regarding micro-mobility technologies
• Concept testing of three storyboards with 50 participants via the dscout research platform
• Pretotype testing on the Duquesne Incline in Downtown Pittsburgh

Beginning to analyze patterns from our research

Concept Testing

In addition to gauging people’s comfort with robots and interfaces on Pittsburgh public transit, we decided to conduct a need-finding activity through storyboarding. We did this by showing participants multiple scenarios involving different levels of virtual attendants along a scale (from most to least anthropomorphic) using the dscout research platform.

For such a futuristic topic, we saw storyboards as a way to help users focus on needs and jobs to be done on transportation, rather than just what form an attendant assumes. Rather than verbally describing our ideas to participants, storyboards allow people to build empathy and step inside the scenarios to better understand what their feelings and needs might be in that specific context.

While designing virtual or robotic attendants is exciting, it is also time consuming and expensive. Storyboarding allows us to explore low fidelity versions of ideas. This empowers users to give more honest feedback while allowing us to iterate rapidly, rather than focusing on a solution which may not truly get at the root of user needs.

A scenario involving a robot attendant on a people mover

Friendly holograms at your service!

Telepresence on wheels, inspired by our team robot, Ohmnibot

Duquesne Incline

While we were developing our pretotype, we asked ourselves some questions we found to be instrumental to our overall progress in this project. After literature review and expert interviews, we decided to bring some questions—

• What do users expect from an attendant?
• What roles do attendants play for passengers?
• At what touch points in a transportation journey might a user need an attendant?
• How do users feel about attendants not being there in-person?

—to riders of the Duquesne Incline in Downtown Pittsburgh.

The view of Pittsburgh from the top of the incline.

So why was the Duquesne Incline picked as our research area? The Duquesne Incline and our idea of a people mover had a lot of overlap in how they function. For example, they both:

• are autonomously driven—there is no operator on board the vehicle,
• are shared vehicles that have transient passengers, and
• move people from point A to point B.

Further, the Duquesne Incline has a set of controlled variables that makes it easier to talk to people about their experiences—a fixed track, speed, and distance from point A to point B. After taking the ride ourselves, we set up our “research station” at the top of the incline.

When representing the remote attendant, we used Wizard of Oz techniques to simulate a person on a screen just by using the back-facing camera of an iPad.

Pretotype Deployment

Our pretotype allowed us to gauge the overall reactions of the form of the attendant. We brought three attendants with us to the incline—a human attendant, a Misty social robot, and an iPad to simulate a telepresent attendant on a screen. We then presented three scenarios to our participants:

1. Asking for directions,
2. Learning more about the incline; and
3. Reacting to a mechanical failure of the incline.

As we presented these scenarios, we asked participants to rank the attendants in order of most comfortable to least comfortable.

Concept testing our three modes of “attendant” at the incline

Synthesis of Pretotype Deployment

After interviewing eight passengers of the Duquesne Incline, we sat down and talked about themes that came up amongst all of them. We learned that:

1. the preferred form of the attendant varies from task-to-task;
2. some participants were wary of the robot because they assumed the robot would automatically video record and they did not want to be video recorded;
3. overall, almost every participant mentioned the value of human-to-human interaction.

What Next?

We decided as a team to pinpoint key personas and transportation journeys to focus on, in order to make the greatest impact with our research. We plan to conduct semi-structured interviews, contextual inquiries (observation, intercept interviews, and directed storytelling), and a longer form diary study focused on these specific populations. By choosing these methods, we will build a collective understanding of in-context transportation experiences and where people most often need assistance or experience low points in their journeys.

By stepping into the daily lives of users on public transport, we will be able to combine our knowledge on the space of AI, robotics, design, and human-computer interaction with our understanding of key pain points and times of need in a transportation context in order to ideate on future solutions for passengers on autonomous people movers.