Ride Vision To Develop 360-Degree Predictive Vision For Motorcycles

Ride Vision, an Israeli start-up has introduced what is being described as the world's first collision aversion technology giving any motorcycle 360-degree predictive vision. Ride Vision can be described as a form of Advanced Driver Assistance System (ADAS), built specifically for motorcycles, which will use predictive vision and warn riders about possible collisions with other vehicles on the road. The Ride Vision safety system has already got the backing of YL Ventures with funding of $ 2.5 million (close to approximately Rs. 17 crore).

"While ADAS accident prevention technologies have become standard in cars, there is great potential to solve this problem for the underserved motorcycle market that accounts for 17% of all driver and passenger traffic fatalities," said Yoav Leitersdorf, managing partner at YL Ventures, who led the Ride Vision funding round.

"Motorbike riders are typically coordinated, alert and very much in tune with the road. We have a special relationship with the road, but unfortunately many factors beyond our control increase risk of collision and the high speed, lightweight, fully exposed nature of the bike makes collisions more dangerous," explained Uri Lavi, CEO & Co-Founder of Ride Vision. "As riders ourselves, we decided to apply our deep learning and advanced vision technology expertise to overcome the cost and technological challenges required to bring any motorbikes and their riders the ability to see, predict and prevent collisions with technology smart enough to leverage standard hardware and cameras, delivering warning signals within 100 milliseconds, without disturbing the rider's focus."

Ride Vision's Collision Aversion Technology (CAT) is described as a fusion of neural network based deep learning platform and computer vision "that enables an accurate, affordable, life-saving solution for any motorcycle, without the need for complex hardware or expensive cameras," a press release from the company stated. The CAT sensors detect threats beyond the rider's sight, predict risk and highlight the relevant threat for the rider, in about one-tenth of a second. Warning lights and audio warnings will then alert the rider of possible collisions with other vehicles.