An accurate vehicle positioning algorithm based on vehicle-to-vehicle (V2V) communications

In vehicular safety applications, a vehicular positioning algorithm plays a crucial role in preventing road accidents through the perception of a dangerous situation in advance. However, most of vehicular positioning devices, e.g. a low-cost GPS receiver, have too large position error to be used for these applications. To reduce this error, we present the vehicular positioning accuracy enhancement (VITAE) algorithm which reduces the position error of a vehicle by taking into account the GPS estimate and the sensing estimate of each nearby vehicle; A GPS estimate is obtained from vehicle-to-vehicle communications, whereas a sensing estimate is measured at local sensors, e.g. vehicular radar. We first present a greedy data-association (GDA) algorithm to find a minimal-weight matching between a GPS estimate and a sensing estimate based on an Euclidian distance. We next adjust the position estimate of the vehicle by using the difference in the mass center of GPS estimates and that of sensing estimates. From the law of large numbers, this adjustment leads to the reduction of the position error by 1/√N times, where N is the number of nearby vehicles. To further reduce the position error, we combine our positioning algorithm with the Kalman filter. From the numerical results, we demonstrate that our positioning algorithm can reduce the position error by 81.8% on average.