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HomeNewsNauto’s AI technology reduces distracted driving amongst Loram’s fleet

Nauto’s AI technology reduces distracted driving amongst Loram’s fleet

The road and rail maintenance enterprise employ computer vision and deep learning techniques to alert inattentive drivers.

Loram, the vehicle fleet company, plans to utilize Nauto’s AI technology to reduce the ill effects of distracted driving in Loram’s fleet of truck drivers. The company will use Nauto’s devices with computer vision and deep learning capabilities to alert unfocused drivers, especially truck drivers.

As per a survey conducted by a truck fleet software company Insight, 4.5 accidents over a year are credited to a single truck driver. With over 14,000 miles covered by Loram truck drivers in a day, there is a significant risk. After exploring several technologies, Grahan Rose, vehicle fleet manager at Loram, decided to proceed with Nauto’s AI and deep learning.

Nauto was founded in 2015 with capabilities in facial recognition and managing distracted driving. Nauto installs a tiny camera device equipped with computer vision technology to capture what is going on the road and what the driver does. 

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The camera does not record everything but the 30 seconds around and a probable collision or driving violation. The device also computes the probability of a collision if the drivers are distracted and alert them when the probability is more than 30 percent. If it notices an incoming collision, it rings an alarm.

Loram has been involved with Nauto since 2018 for overall road traffic management and monitoring drivers’ behavior. Rose said, “It’s significantly decreased our tailgating, our cellphone usage. Our guys are going hands-free and not picking up their cellphones and getting distracted when they’re driving.”

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Disha Chopra
Disha Chopra
Disha Chopra is a content enthusiast! She is an Economics graduate pursuing her PG in the same field along with Data Sciences. Disha enjoys the ever-demanding world of content and the flexibility that comes with it. She can be found listening to music or simply asleep when not working!


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