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Crowdsourcing real time street-level data with token incentives via Augmented Reality and Machine Learning

Photo of David Hodge
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BULVRD is using Ethereum tokens and smart contracts to build and incentivize community mapping and navigation ecosystem. Continuing to differentiate by leveraging Augmented Reality and Machine Learning to automate data streams other apps would require 4 - 5 clicks from a user to create.

Our Augmented Reality feature named BULVRD IQ taps into the latest tech to create an all new mapping and navigation experience. Providing the end user with a real world visual of their next navigation turn, real time safety alert for detections like following a vehicle too close or a pedestrian entering the cross walk, lane tracking, and street sign detection.

Extending our machine learning intelligence,  we automatically detect or validate road reports like traffic, road hazards, construction, and pot holes. Compared to other community mapping apps which require the driver to take their eyes off the road and click 4 to 5 buttons, this helps enable a safer hands free driving experience.

A latest development uses our lane tracking technology to intelligently detect when a drive is moving slowly in the left lane (among other conditions met such as no traffic, hazards, etc) and encouraging them to move to the right lane by rewarding them with a token micro-transaction once the lane change has been completed.

To put it simple, BULVRD can see what other mapping and navigation apps simply cannot! We believe this technology can be used in a wider range by providing more localized driver awareness of local road and traffic laws. Helping reduce traffic congestion and providing a safer, rewarding driving experience. 

How will your solution benefit residents, workers, or visitors in the Michigan Central Station impact area? (1,000 characters)

Anyone with a smartphone can use BULVRD while in a vehicle can earn tokens by contributing to the ecosystem. The real time street level insights generated can provide: - Deeper insights into traffic flows with visual context - Provide a new reference point for first responders before arriving to a scene (fires detected, people detected in the area, visual snapshots) - Track historical changes overtime and detect anomalies (ex fallen / missing street signs)

Describe your solution's stage of development

  • Ready to Scale - you have completed and expanded your pilot and are seeing adoption of your solution by your intended user

Insights from previous testing (500 characters)

- Working directly with rideshare drivers (Uber, Lyft, etc) to help improve their days of 6+ hour driving. - Michigan PlantM Grantee - During our beta the community covered 500m+ miles - 3 million road reports (traffic, construction, etc) were generated.

Tell us about your team or organization (500 characters)

BULVRD is a US company, founded in late 2018. With initial pre-seed investment from ConsenSys Ventures. Recently launched our Android offering fully public in partnership with Samsung and their newest blockchain initiatives. Including integrations with latest devices in 7 countries.

Size of your team or organization

  • 2-10

Team or Organization URL

Are you submitting as a student team?

  • No

Are you submitting as a team from the Impact Area?

  • No

Funding Request

  • $50,000

Rough Budget (500 characters)

Rough use of the budget would be to work with local officials to identify key KNOWN road laws they'd like to remind / make drivers aware of. We'd then train our machine learning models to enable this. This would also include on-site testing and validation of new implementations. We'd also like to run some general awareness programs of our offering, coupled with in-app achievements / rewards for contributing.

Describe how you would pilot your idea (1000 characters)

During this pilot, we'd optimize the experience for the general impact area. Providing more tailored experiences to benefit those in the area. This would include also working with local officials and partners to optimize street level insights and leverage them in test environments to refine and improve the impact we can have.

Describe how you would measure the success of your pilot (1000 characters)

We currently measure growth by how many miles are driven each day by our growing community. We'd apply a similar approach here, but look to put an emphasis on reaching more specific targets. If the community can cover major throughways at-least twice a day providing a true real time insight stream, we'd consider that a success.

Sustainability Plan (500 characters)

Our long term roadmap includes a core focus on using location based map advertising as a revenue stream. Leveraging visual and automated geofences to enable a new means of user reach. Our average in-app session times of 30+ minutes twice a day provides a great opportunity to reach users in a hyper-local way.

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Join the conversation:

Photo of Mackenzie Fankell

David Hodge Thank you for posting this proposal! I am a Michigan Central Station challenge facilitator. This looks like an impressive idea. We have had a lot of community members express their concerns with pedestrian and bike safety within our impact area. It would be great to see if pedestrian and bike safety could be a focus for motorists in the pilot stage!

Photo of David Hodge

Mackenzie Fankell Appreciate you taking a look!

For non-motorist detection, our models are trained for pedestrians and crosswalks already. Our safety alerts are currently looking for pedestrians in view, though putting a larger emphasis on non-motorist could be a great focus during the pilot stage. Some deeper features around crosswalk reminders (pedestrian right-of-way) would be nice additions.

With some in the field work we could add in bike safety features. Maybe detecting and highlighting bike lanes to a motorist, or detecting an approaching bicyclist and reminding the driver to give them room?

Photo of Mackenzie Fankell

David Hodge  Thank you for your response! A lot of bikers have reported that bike lanes in Detroit can start and end abruptly, so drivers and bikers may not be aware of the sudden change. This makes for a dangerous situation, so alerting drivers could definitely help! I look forward to hearing more about your thoughts on what features to implement during the pilot phase. Crosswalk reminders, highlighting bike lanes, and detecting cyclists all sound like very promising features!

Photo of David Hodge

Mackenzie Fankell From what we've seen, most (if not all) maps lack a lot of visibility in regards to bike lanes. So on top of use our AR / ML tech to detect these in view, we can 'embed' these findings directly into the map themselves. This could help motorist and bikers alike when in comes to general mobility around town.

Photo of David Hodge

Some video previews of our latest developments with our Augmented Reality / Machine Learning Tech (BULVRD IQ)

Photo of David Hodge

Example of safety insights we can capture to identify key hotspots of 'unreported' near collisions including pedestrians: