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TPMS & Electronics

Continental pilots real-time tread depth monitoring

Lawrence ButcherBy Lawrence Butcher11th December 20202 Mins Read
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Continental says it has successfully demonstrated real-time tire tread depth monitoring in a pilot using vehicles from carsharing provider Share Now Denmark.

In cooperation with project partner and telemetry specialist Traffilog, the company merged feeds from tire sensors and telemetry data from Share Now’s fleet of cars with a proprietary algorithm in the cloud. As a result, Continental claims it can predict tire tread depth in real time to an accuracy of 1mm. It hopes that in future, smart digital tire solutions such as this will be a key driver in enabling need-based servicing of tires, instead of regular, scheduled servicing.

“With this pilot we are first in the market to offer significantly accurate tread depth monitoring via an algorithm. With an accuracy of below 1mm our pilot has in fact set an industry benchmark in smart digital tire solutions”, said Tansu Isik, head of business development and global marketing at Continental Tires. “We have worked very closely with our Continental Automotive Technologies colleagues as well as with our partner Traffilog to achieve this success.”

For fleet providers accurate tire condition information could be highly beneficial to efficiency. “Real time, telematic based tire monitoring is a huge benefit for us. We can monitor and service our tires at any time and take actions proactively,” said Steen Herløv Andersen, head of operations at Share Now.

The real-time tread depth measurement pilot is part of a wider project in which Continental, together with Traffilog, is delivering real-time remote tire pressure and temperature monitoring of the entire Share Now fleet, with approximately 600 cars currently in operation.

Continental notes it was able to integrate its real-time tire monitoring solution into an existing telematic setup. To enable both capture and transfer of data, Traffilog modified its telematic unit to fit Continental’s requirements in order to harness the relevant data feeds for its proprietary algorithm.

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