When you stop to think about it, it's amazing how integrated GPS technology has become with our everyday lives. We're constantly using it to get directions and countless smartphone apps use it to suggest nearby restaurants, show us how far we've run and more.
First developed in the 1960's, the Global Positioning System allows a user to determine their location by measuring the time it takes to receive radio signals from four or more overhead satellites. Receivers in our smartphones, navigational systems and more can compute location and velocity based on these measurements. Standard GPS is accurate to within 10 meters, while Differential GPS has improved accuracy to one meter.
While this has been wonderfully sufficient for what we've used the technology for so far, self-driving cars will require even more precise location measurements.
"To fulfill both the automation and safety needs of driverless cars, some applications need to know not only which lane a car is in, but also where it is in that lane—and need to know it continuously at high rates and high bandwidth for the duration of the trip," said Jay Farrell, professor and chair of electrical and computer engineering in University of California, Riverside's Bourns College of Engineering
Farrell and his colleagues have tackled this issue and have developed a way to process data from GPS that delivers accuracy down to a few centimeters. Not only is this method more precise, but it's also more computationally efficient.
According to UC Riverside, "The approach involves reformulating a series of equations that are used to determine a GPS receiver's position, resulting in reduced computational effort being required to attain centimeter accuracy."
This improved process can be used in the development of not just self-driving cars, but improved aviation, naval navigation systems and precision farming. Of course, it will likely also make its way to our gadgets, like smartphones and smart watches as a way to provide even better location data without requiring more processing power.
In order to improve navigational systems in self-driving cars, this new technology will need to be combined with an inertial measurement unit (IMU) to achieve high sample rates and high bandwidth continuously. So far, this combination hasn't been possible because of the need for greater processing power, but because this new method uses several orders of magnitude fewer computations to process the GPS data, this could finally be possible.