Project Overview
Spring 2022Team:
Yang Cheng, Anisa Jibrel, Vinod Redd, Jackie Wang
My Role:
Motion graphic design, Filming
Duration:
4 weeks
Process:
Concept Mapping| Storyboard Speed Dating | Usability TestingThe context of this project is in 2035, in which my group and I are IoT specialists, designers, engineers, and entrepreneurs who are seeking to catalyze a preferable future for cyclists. Amidst social and environmental pressures—but with technological possibilities that enable new ways to connect people, places, and things—we created foresight, an integrated IoT service that protects cyclists from road hazards through leveraging aggregated data and the power of civic engagement.
Concept Map
Our ideation phase began with the selection of our topic: community aid and emergency response. We then dived deep into brainstorming to pinpoint relevant concepts in this problem space.
Specifically, we decided on creating a safer biking experience with a smart road condition tracking system.
System Breakdown
Geolocation sensor
Uses ubiquitous 6G technologies to deliver precise and rapidly-updating location data.
It also syncs up with the AR glasses to provide timely updates and alerts on moment-to-moment changes in road conditions.
Smart AR glasses
Display digital alerts of upcoming potholes and dangerous intersections.
Community-powered data
Harnesses the power of the community. Data gathered from other cyclists' sensors will be uploaded to a cloud for route planning and infrastructure level change.
The App
As you saw in the video after every ride, the app will prompt the user to confirm auto-detected potholes and accidents, using ML algorithms to better the prediction over time. The user confirms and has the option to give more details, which is sent to the foresight server (active for all users) and the government.
The app closes the loop from IoT detection to making meaning out of information. But we know that machine learning algorithms could get it wrong, so the human confirmation helps us to categorize the particular issue as well as adding more qualifying information that is useful for all.
The app closes the loop from IoT detection to making meaning out of information. But we know that machine learning algorithms could get it wrong, so the human confirmation helps us to categorize the particular issue as well as adding more qualifying information that is useful for all.
Impact
Currently, cyclists face road hazards solo. There isn’t a way to warn fellow cyclists. Someone can fall because of the same pothole and 20 mins later someone else could fall victim to it. There is no connection within the community. When cyclists do report them, it’s unclear when/if the city government will fix them.
In the future, when people bike with foresight’s smart glasses, they create vibration data as a byproduct. In this model, we empower cyclists to create data to help each other easily. For the city government, foresight acts as the communication bridge between cyclists and the government. This is a win-win model that creates value for all stakeholders involved.