Ride Vision Technology
Using Predictive Vision to Save Riders’ Lives
Ride Vision is a unique Advanced Rider Assistance System (ARAS) tailored to meet the safety needs of riders.
Our industry-leading Collision Aversion Technology (CAT™) is a fusion of Artificial Intelligence (neural networks) and Computer Vision, and is designed to seamlessly integrate with all motorcycle and scooter makes and models.
Motorcycles are quick and agile. They lean, maneuver quickly on the road, and sometimes even navigate between other vehicles. Their lightweight builds and nimble maneuvering means they experience greater effects of physical force and roadway vibrations. Even basic navigating in traffic requires a rider to shift their motorcycle’s weight from side to side and from front to back (when braking and accelerating).
We created Ride Vision to meet the safety needs that no other technology could provide for the two-wheeled market. Ride Vision’s core technology is based on the unique fusion between software and hardware that is optimized for two-wheelers and their behavior on the road. Ride Vision was designed to act as a non-intrusive Ride Assistance System. Unlike dangerous and complex auto-controlled systems, Ride Vision provides riders with enough time to react on their own, and make the safest choice possible.
Ride Vision functions via the coordination between two wide-angle cameras, our patented Predictive Vision algorithm, a compact in-unit computer processor (ECU), and two unobtrusive mirror-mounted LED alerts (the operations of which are fully customizable within the Ride Vision app).
Using Ride Vision’s 360° wide-angle camera footage, our Predictive Vision algorithm analyzes the visual data within the system’s ECU to identify only critical threats to the rider. This selective alert system eliminates the need for expensive and cumbersome hardware.
Our CAT™ system is built to work with any make and model of two-wheeled vehicles, significantly reducing production cost and providing freedom of modularity to manufacturers.