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New GPS/IMU fusion system improves the precision of GPS in vehicles in cities by up to 90%; implications for connected vehicles, autonomy and ITS

12 February 2013

Marti1
Integration of GNSS (GPS) antenna of rover receiver and IMU in a platform over the roof of the test vehicle. Source: Martí et al.Click to enlarge.

Researchers at Universidad Carlos III de Madrid (UC3M) have developed a new system which improves the ability of a GPS to determine a vehicle’s position as compared to that of conventional GPS devices by up to 90%, and which can be installed in any vehicle at a very low cost. Future applications that will benefit from the technology will include cooperative driving, automatic maneuvers for the safety of pedestrians, autonomous vehicles or cooperative collision warning systems, the scientists noted.

The system is a fusion of a GPS (or GNSS, Global Navigation Satellite System) and an Inertial Measurement Unit (IMU) integrating three accelerometers and three gyroscopes, based on a context-aided Unscented Kalman filter for navigation in urban conditions. The GPS/IMU system was jointly developed by the Applied Artificial Intelligence Group (GIAA – Grupo de Inteligencia Aplicada Artificial) and the Systems Intelligence Laboratory (LSI – Laboratorio de Sistemas Inteligentes) 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.

—David Martín, 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 such as buildings, trees, or narrow streets.

The greatest problem presented by a commercial GPS in an urban setting is the loss of all of the satellite signals. In certain cases, such as in tunnels, communication is lost, which hinders the GPS’ applications reaching Intelligent Transport Systems, which require a high level of security.

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. 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.

—David Martín

In the case of the new prototype that they have developed they have managed to guarantee the position of the vehicle to within 1 or 2 meters in urban settings.

Marti2
System architecture. In general, information flows from left (sensors) to right (output of estimation processes). The main exception resides in the module that infers contextual information, the products of which are accessible to every other component in the system. The result of attitude estimation is fed back into the sensor refinement process.

The figure shows how data moves around the system. Solid lines represent raw data as captured by the sensors, blue dotted lines refers to refined sensor information (original data that has been processed to compensate known errors, or to produce features that summarize a number of raw values). Dashed lines are reserved for new data that has been obtained by advanced processing techniques such as filters or reasoning algorithms. Martí et al. Click to enlarge.

This software is based on an architecture that uses context information and a powerful algorithm [Unscented Kalman Filter] that eliminates the instantaneous deviations caused by the degradation of the signals received by the GPS receiver or the total or partial loss of the satellites.

—Enrique Martí, of UC3M’s GIAA

Currently the researchers have a prototype that they can install in any type of vehicle. In fact, it is already working on board the IVVI (Intelligent Vehicle based on Visual Information), a real car that has become a platform for research and experimentation for professors and students at the University. The objective of the researchers from LSI and UC3M who are working on this “intelligent car” is to be able to capture and interpret all of the information that is available on the road, and that we use when we are driving. To do this, they are using optical cameras, infrareds and laser to detect whether we are crossing the lines on the road, or whether there are pedestrians in the vehicle’s path, as well as to adapt our speed to the traffic signals and even to analyze the driver’s level of sleepiness in real time.

The next step these researchers intend to take is to analyze the possibility of developing a system that makes use of the sensors that are built into smartphones, since intelligent telephones are equipped with more than ten sensors, such as an accelerometer, a gyroscope, a magnetometer, GPS and cameras, in addition to WiFi, Bluetooth or GSM communications, for example.

We are now starting to work on the integration of this data fusion system into a mobile telephone so that it can integrate all of the measurements that come from its sensors in order to obtain the same result that we have now, but at an even much lower cost, since it is something that almost everyone can carry around in his pocket.

—Enrique Martí

Resources

  • Enrique David Martí, David Martin, Jesús García, Arturo de la Escalera, José Manuel Molina, José María Armingol (2012) Context-Aided Sensor Fusion for Enhanced Urban Navigation. Sensors Volume: 12 Number: 12 Pages: 16802-16837 doi: 10.3390/s121216802

February 12, 2013 in Autonomous driving, Connected vehicles, Driver Assistance Systems, Intelligent Transportation Systems (ITS), Vehicle Systems | Permalink | Comments (1) | TrackBack (0)

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Comments

Very interesting for future automated smart cars. All of it could be integrated into the car's display/data center (tablets or smart phones) at little extra cost.

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