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GPS Modeling for Vehicle Intelligent Driving Simulation

Journal Article
2018-01-0763
ISSN: 2574-0741, e-ISSN: 2574-075X
Published April 03, 2018 by SAE International in United States
GPS Modeling for Vehicle Intelligent Driving Simulation
Sector:
Citation: Gao, X., Deng, W., and Wang, J., "GPS Modeling for Vehicle Intelligent Driving Simulation," SAE Intl. J CAV 2(1):57-65, 2019, https://doi.org/10.4271/2018-01-0763.
Language: English

Abstract:

In recent years, intelligent vehicles have become one of the major research topics in vehicle engineering and have created a new opportunity for the automotive industry. Simulation and real experiment are both essential to the development of intelligent vehicle technologies. Vehicle positioning systems, such as global positioning system (GPS), play an important role in intelligent vehicle development. The GPS model plays a major part in the development of intelligent vehicle simulation systems. Primarily focusing on application requirements of intelligent vehicle simulation platforms for GPS sensor modeling, considering the major factors affecting positioning accuracy in vehicle driving environments, this article establishes a new GPS model and algorithm based on the physical and functional characteristics of GPS. As the basis of this model system, a precise ephemeris model is established to obtain the coordinates of GPS satellites at any given time. A new occlusion model is proposed in order to describe external environmental disturbance more effectively. This model takes into account not only the emission angle of the satellite signal and the shadow of the earth but also the blocking from objects in the scene, such as buildings, trees, and other shelter. By using the Doppler effect and solving the pseudorange equation, velocity and position are calculated. And in the process of measurement, the pseudorange error and the frequency shift error are taken into account. The proposed GPS model has good fitting precision, and the performance of the entire system is verified through comparison with the measurement data from a real GPS sensor.