Machine Learning Times
Machine Learning Times

1 year ago
Lime Uses Sensor Data to Keep Scooters Off Sidewalks


Lime is rolling out sidewalk detection

Originally published in VentureBeat, January 28, 2020

While the burgeoning urban micromobility movement offers many advantages over traditional transport, the unbridled proliferation of electric bikes and scooters also comes with major downsides — such as cluttered and hazardous sidewalks.

Against this backdrop, VC-backed Lime today announced a new approach to keeping riders off the sidewalk, using a mixture of sensors and an AI-based statistical model that predicts the likelihood a user was riding on the sidewalk and for how long.

For the pilot initiative, Lime is partnering with San Jose, California, a city that has been trying to crack down on rogue scooters by regulating the companies behind them. This effort aims to control the number of scooters that are deployed, ensure sufficient safety protocols are in place, and require scooter companies to keep riders off the sidewalk — something Lime is setting out to tackle with today’s announcement.

“Lime has been working on sidewalk riding detection since hearing concerns from some city and community partners, and we believe we may have finally cracked the code on this issue and developed a technology that is effective, safe, and scalable,” said Lime’s northern California general manager, EV Ellington.

How It Works

Lime scooters already have onboard speedometers, but combining them with accelerometer data could allow the company to understand the vibration of the underlying surface using a “sophisticated statistical model” it designed in-house. This should enable it to distinguish between sidewalks and roads.

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