Continental released a statement that the company has successfully demonstrated tyre tread depth monitoring solution in real time using a pilot vehicle from Share Now Denmark.
With the cooperation of telemetry expert Traffilog, the company evaluated and gathered feeds from tyre sensors. They also considered collecting telemetry data from the Vehicles provided by Share Now's fleet of cars with the help of algorithm in the cloud.
Continental claims that it can predict tyre tread depth accurately as close as 1mm with the help of the tyre tread depth monitoring solution. The tyre manufacturer is in the hope that in the near future, smart digital tyre solutions like the tyre tread depth solution for real time is the key to enabling need-based servicing of tyres instead of the 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 Tyres.
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"We have worked very closely with our Continental Automotive Technologies colleagues as well as with our partner Traffilog to achieve this success," He added.
Accurate tyre information could be highly beneficial for fleet owners to have an efficient fleet. "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 Herlov Andersen, head of operations at Share Now.
Continental, together with Traffilog, is keen to provide the real-time tread depth measurement solution as a part of a wider project to the entire Share Now fleet of approximately 600 cars currently in operation.
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Continental noted that the company was able to integrate its real-time tyre monitoring solution into an existing telematic setup. To enable both capture and transfer of data, Traffilog modified its telematics unit to fit Continental’s requirements in order to harness the relevant data feeds for its proprietary algorithm.
