Summer Sprint 1: Putting it in Park
Wrapping up spring semester and driving full speed into summer!
Written by the MHCI x 99P Labs Capstone Team
Edited by 99P Labs
The MHCI x 99P Labs Capstone Team is part of the Master of Human-Computer Interaction (MHCI) program at Carnegie Mellon University.
If you missed the final sprint from spring semester, read it here!
Navigating the end of the semester
We’ve finally made it to the end of the spring semester! As a quick recap, our team was able to finish synthesizing our remaining research, flesh out insights, prepare a research report and website, begin designing prototypes, and present an exciting final spring presentation to the 99P team! After all of these exciting endeavors, we wrapped-up the first phase of the project with a collaborative client session where we discussed all the exciting possibilities moving into summer.
Transitioning from our spring presentation
Ultimately, the many iterations of our spring presentation led us to approach our problem space differently — through the lens of emerging technologies. Our primary research focused on examining passengers onboard public transportation of the current state. From this, we gathered a lot of data about the existing friction points of today’s transit journey. Through our spring presentation, we made the pivot to combining what we learned through observation with what we know through secondary research. This led us to more applicable spaces of the realm of autonomous vehicles.
We took what we learned from our literature review — affective computing, haptics, human-robot interaction — and applied it to our potential prototype ideas and our problem space. This led us to ideas like leveraging affective computing to read body language of users and having attendants cater to those needs. Further, engaging different human senses — such using haptics to communicate via touch — allows for more accessibility and multiple modalities for communication.
What we learned?
Before we jump into what we’ve been working on in the summer, let’s first highlight some of the important spring semester insights we developed that will guide us as we begin designing prototypes.
The first important learning is that rather than an operator or attendant being the sole resource people look to for information, assistance, or having their needs met, there is already an existing ecosystem of attendants.
In other words, what’s helping passengers meet their needs isn’t necessarily the driver after all. Our research showed that an attendant can actually be defined as an entity that supports passengers in meeting their needs in their transit journey, such as a personal device or built in infrastructure. These things make up an already existing ecosystem of attendants, and this idea of an adaptable ecosystem leads the way to our remaining insights.
Modality matters: Passengers prefer different ways of asking for and receiving information depending on their current need, such as pressing a button to signal a stop or watching an in-vehicle screen to figure out they’d reached their destination. Furthermore, they want multiple options that remain consistent and accessible.
Inter-passenger dynamics is a balancing act: A passenger’s perceived sense of safety on their journey is most often impacted by the number of fellow riders–people usually don’t want to situate themselves on either end of the passenger number spectrum. Factors like passenger proximity, noise, crowds, available seating, influence the riding experience.
There’s no place like home: A passenger’s familiarity with their route and surroundings is a critical part of feeling comfortable and confident during a transit journey. Familiarity allows for passenger independence, and lack of familiarity leads to seeking out interactions and information. In the context of adopting new technologies, perceived usefulness* and ease of use are key to reducing hesitation around unfamiliarity of technology.
Reactive not proactive: Passengers desire a discreet way to receive and request information regardless of familiarity level. Individuals can operate in different passenger modes, whether that’s as a tourist or a routine commuter, but regardless of how familiar they are to their destination, they all desire system feedback that is unobtrusive.
Shifting our gears into summer
For our first sprint, our learning objectives were to figure out if passengers want to pre-select their seats before they board a vehicle and if passengers would like to customize their seatings. These customizations include setting up privacy partitions, rotating their seats, and adding/removing seats. This is an important starting point for the team because it will inform how the screens will be designed based on seating configuration.
To test for this, we did a pop-up co-design session where we set up chairs in Schenley Plaza and asked participants to react to seating arrangements. We gave them three scenarios to respond to. How would you want to rearrange the seats if you were traveling with
- One other person you do not know?
- Two of your friends?
- One friend and 2 strangers?
What’s next for us?
As we go from sprint to sprint, we plan to focus on one component of the ecosystem at a time.
And with that, we’re back on the road! Stay tuned for all the exciting stops and detours that are still to come.
Follow 99P Labs here on Medium and on our Linkedin for future updates on this project and other student research!
