Transformation: Redundant, accurate, hack-proof location data reduces reliance on GPS data: which can be inaccurate in urban or natural canyons; can be jammed with low-cost jammers; can be spoofed with bogus location data broadcast on a stronger frequency than the satellite data; and consumes significant energy to communicate with the satellite.
Transformation: Vehicles do not have to rely on visual information to navigate streets safely. RF-eye beacons provide information to vehicles when signs are occluded (trees, other vehicles on the road, etc.) Vehicles are informed of infrastructure information even in low light and inclement weather situations (nighttime, snow storms, etc.) RF-eye beacons train ML algorithms what road infrastructure objects look like in unusual conditions (fog, smoke, etc.,); over time, reducing “confusion” in autonomous vehicles. Vehicles understand painted road safety markings even when the lane lines are not visible; understand exactly what lane they are in on a multilane highway; and are able to navigate more accurately and with lower power consumption.