The system, which is based on sensor fusion, incorporates a conventional GPS signal with accelerometers and gyroscopes in order to reduce the margin of error in establishing a location. It was jointly designed and developed by the Applied Artificial Intelligence Group (GIAA) and the Systems Intelligence Laboratory (LSI) at UC3M.
“We have managed to improve the determination of a vehicle’s position in critical cases by between 50 and 90 percent, depending on the degree of the signals’ degradation and the time that is affecting the degradation on the GPS receiver,” said David Martín, a researcher at the LSI.
The margin of error of a commercial GPS, such as those that are used in cars, is about 15 meters in an open field, where the receiver has wide visibility from the satellites. However, in an urban setting, the determination of a vehicle’s position can be off by more than 50 meters, due to the signals bouncing off of obstacles like buildings, trees, or narrow streets, for example. In certain cases, such as in tunnels, communication is lost, which hinders the GPS’s applications reaching Intelligent Transport Systems, which require a high level of security. “Future applications that will benefit from the technology that we are currently working on will include cooperative driving, automatic maneuvers for the safety of pedestrians, autonomous vehicles or cooperative collision warning systems,” said Martín.
The greatest problem presented by a commercial GPS in an urban setting is the loss of all of the satellite signals. “This occurs continually, but commercial receivers partially solve the problem by making use of the urban maps that attempt to position the vehicle in an approximate point,” he said. “These devices can indicate to the driver approximately where s/he is, but they cannot be used as a source of information in an Intelligent Transport System like those we have cited.” However, in the case of the new prototype that they have developed they