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SAIVE

// UX //

60 second snapshot

The goal

SAIVE (smart artificial intelligent vehicle ecosystem) is a near-future concept where vehicles and commuters communicate with each other while automatically recording and broadcasting data.

The users

Our primary users were cyclists however, every actor on the road would benefit from SAIVE.

The team

I worked with Ananda Annisa Prasetyanto and Qiao Yin. Special thanks to Carly Burton at Cognizant.

Concept video

Unfortunately the video itself is over 60 seconds.

 

Process

Research

Interviews

Our team conducted interviews with commuters (cyclists, motorists, and pedestrians) as well as experts in cycling and traffic flow in order to gain insights on the current transportation system. Cyclists are primarily subordinate actors on the road and on the sidewalk, so we decided to focus our effort on improving their experience.

Directed storytelling

We asked commuters to walk us through their daily commute with Google Maps. Using contextual inquiry about the challenges users face on the road may have proven dangerous.

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Spatial observation

We decided to spend time observing the interactions between bikers, pedestrians, and motorists in order to see how accurate interviewers were in describing the relationships between those groups.

Design sprint

Qiao Yin led the abbreviated sprint, which was completed over the course of a weekend.


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User journey

Mapping a cyclist's full experience from start to end, while seeming unnecessary at times, helped ensure that we would be maximizing our impact for urban cyclists.


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Idea generation

After determining the specific part of the user journey we wanted to address, we sketched out new ideas every 30 seconds using the “Crazy Eights” brainstorming framework.


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Project framing

After identifying our problem space, we decided to check back with our project brief, which was to implement AI into a transportation system. We decided to focus on a relatively near-future concept, that of “alerts” and “bike visibility”.


Interaction mapping

After deciding on a large number of interactions we wanted to support, selecting only a few to illustrate with our concept video was the final step. Choosing interactions which were easy to demonstrate while having a large impact was our sweet spot.


 

Concept details


Turn signals

If SAIVE predicts that a car’s near-future trajectory will impact a nearby person, their turn signal will automatically flash, even if the driver hasn't turned on their blinker. The key is that false positive turn signals have minimal consequences.


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Dooring

"Dooring" is when the opened door of a parked car collides with a biker. If SAIVE predicts this will happen, a notification will be projected onto the car window and the door will be temporarily locked.


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Halo'd smart phones

A "halo'd" object is similar to a smart object, constantly producing data about it's environment. In response to passionate interviews, we intentionally chose to halo the smart phones of cyclists instead of the bikes themselves. Location and velocity data can be captured by a smart phone in the pocket or backpack of a cyclist. 


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Driver data

Drivers already have the potential to be predictable through normal driving activity. The speed of the car, angle of the steering wheel, weight on the seat cushion, and many other variables can already be analyzed.


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DSRC

DSRC, "dedicated short range communication", is similar to bluetooth technology and is the backbone of SAIVE. The implementation of DSRC within the ecosystem requires a vast degree of edge computing which isn't possible with today's technology. As this was a future concept, we didn’t see a problem with this constraint.


Technology already in cities

Autonomous vehicles and smart traffic signals, both which have been selectively implemented in Pittsburgh, collect vast amounts of data, adding a wealth of information to SAIVE.