IDEO Co-Lab Makeathon: Carl

In October, I was selected from hundreds of applicants to spend a day at IDEO CoLab to participate in their makeathon with fifty others. It was a day full of forming new friendships, post-it-ing, and sprinting to design new solutions to difficult problems in an emerging space. We were assigned a team of 4 other people with backgrounds in design, tech, and engineering from all stages of life.

 

 

 

OUR BRIEF:

 

How might we crowdsource real driving data to improve autonomous vehicle systems?

 

Ideas listed included LIDAR and camera data in cars.

 

PROCESS:

 

Our team went through the entire design process, from understanding the prompt, to defining the user journey, creating how might we statements, ideating, and then finally designing and prototyping. Design is democratic, so we spent a lot of time discussing ideas and voting to narrow down the scope to create a feasible product.

There were originally 4 types of potential consumers we wanted to understand:

  1. the commercial driver: fedex, ups, truck drivers.
  2. the commercial driver: rideshare drivers
  3. the personal driver: driving for pleasure & work
  4. the autonomous vehicle (AV) companies that needed data

We had so many ideas for data points and platforms, from building sensors into headlights to incentivizing drivers to share data by helping them find lost pets (!). Ultimately we narrowed down to helping commercial rideshare drivers and AV companies by creating a marketplace platform for drivers to integrate data from their driving to sell to companies securely through the blockchain.

 

 

THE DATA CONSUMER:

Meet nuTonomy, an AV startup.

nuTonomy is helping develop the next generation of autonomous vehicles, but they need data to train their autonomous vehicle systems.

Their needs:

Quantity of data: Companies like nuTonomy do not have the car fleets to amass the  quantity of data needed to train AV systems

Quality of data: Engineers need data from a specialized suite of sensors to train AV systems.

Communicate with the data gatherer: Companies like nuTonomy need to communicate with data gatherers obtain data on specific maps.

 

THE DATA GATHERER:

Meet Ralph, a Lyft driver.

Ralph is trying to make the most of his time working as a Lyft driver in the Bay Area

Their needs:

Make more revenue: Ralph is trying to maximize his revenue from working as a Lyft driver.

Locate riders: Ralph needs help locating passengers in congested urban areas and at night.

Improve safety and convenience: Safety for Ralph, his passengers, and other pedestrians.

 

 

 

 

IDEA:

Car Add-on

Carl is a car add-on that can create and collect all type of data, including utilizing a built-in LIDAR system that detects surroundings and is the key technology in autonomous vehicles.

For drivers, Carl will enhance the ridesharing experience by helping drivers like Ralph find passengers, earning extra revenue, and create a safer experience for drivers and riders. The built-in screen can serve a variety of purposes.

For AV companies, Carl will allow AV companies to amass high quality, specific data for training, and spend less time and money collecting or purchasing data from other organizations.

Mobile Interface

The main interface that drivers will interface with is obviously their ride-sharing apps. We wanted to be a stand-alone app that runs in the background for convenience and also to not interfere with current infrastructure. The purpose of the app is to allow drivers to have full transparency over what type of data is going to be shared and uploaded with companies.

  1. Drivers start in their ride-share apps, accepting a requested ride. Like how navigation apps start after rides are accepted and then run simutaneously, Carl also runs in the background.
  2. If this was a first-time user, the first onboarding step is to personalize your “Carl,” essentially naming your car. We understood that collecting and sharing data along with autonomous vehicles could be a scary concept, so wanted to make the process as personable and humanistic as possible. In this example, we’re naming the car Ana.
  3. This main screen shows what the data will look like through the LIDAR system, overlaying a 3D projection map of the collected data on top of real-time video. This is just an example of the data, and can also open it up to more views on different data sets.
  4. The “Start Earning” screen is the most important to drivers if their incentive is to make more money. On this screen, the driver can see how much they earned and any stats related to that.

Web Interface

On the other side of the interactions, the AV companies want to be able to select and receive this data in an easy way. Our “marketplace” allows for these companies to easily request for data in areas they currently lack information on. They can simply highlight or search the areas that they want more data on, and the system will match up their request to the routes that current ride-share drivers are on, and the drivers will be paid after the request is made.

 

Tools Used:

Old Fashioned Foam Core, Sketch, Origami, After Effects, Invision. & a lot of post-its.

 

Thanks:

Special thanks to Teddy, Nicole, Olivia and Thomas who were all on my team. Nicole worked on the web interface while Teddy & Olivia worked on the physical prototype. Thomas worked on the branding and screen assets, while I created the mobile prototype with the live video functionality.