Velovision is an Advanced Cycling Assistance System for bicycles, e-bikes, and more.
We believe that cyclists deserve the same level of AI-powered protection and assistance that is provided to car drivers.
Velovision is a pioneering active safety system for cyclists, easily accessible to anyone with a bicycle, iPhone, and Rearview.
A revolutionary proactive safety system.
Gives you helpful warnings.
Velovision knows the passing distance and whether the overtake will happen to your left or to your right, so it won't unnecessarily warn you about vehicles that are passing two lanes over. Velovision doesn't miss cyclists or other smaller, slower vehicles that radar often can't see. It gives you a better understanding of the threat even before you look at the live view to check.
The overtake prediction algorithm is customizable to your riding style.
Helps you make split-second decisions.
Velovision shows you what’s behind you, without requiring you to turn your head, potentially veering off your line. Just glance at the phone screen for an easier and safer way to look behind you, whether to take evasive action, or just to check that you haven’t accidentally dropped your riding buddy.
“Detection View” provides a birds-eye visualization.
“Live View” provides a full-screen display.
Uses cameras to prevent incidents, not just record them.
By itself, Rearview is an always-on video camera. Unlike all other action cameras, however, it has a powerful processor and highly optimized software that allows it to stream the video wirelessly to iPhone with almost no delay. Velovision uses that real-time video stream to give you timely overtake warnings.
Of course, recordings are still available.
Designed for cyclists, by cyclists.
Cycling demands unique technical solutions that are suited to the specific environment and culture of cycling. Legacy technologies like radar were never meant for cycling, often feeling shoehorned in. We took the lessons of self-driving technology in the automotive field, and built Velovision from the ground up for cyclists. Our team consists of avid roadies, mountain bikers, cyclocrossers, gravel grinders, and fixie foos. We insist on building dedicated hardware, software, and deep learning systems trained on custom cycling-specific datasets.
