Abstract
In this paper, a novel framework is developed with the intention of continuously predicting vehicle position even in the challenging environments such as partial and full GPS outages. To achieve this, the Bayesian-Sparse Random Gaussian Prediction (B-SRGP) approach is proposed where the sparse random Gaussian matrix which obeys the restricted isometry property with high probability is adopted to handle the measurement model. During the full GPS outages, the proposed method fuses all available INS measurements to improve the vehicle position prediction whereas in free outages only the GPS data are processed. Besides, the Bayesian inference is used to specifically deal with the vehicle position prediction in partial GPS outages where data from both GPS and INS are taken as inputs. In all cases, measurement noises are assumed to be non-Gaussian distributed and follow the generalized error distribution. The performance of B-SRGP is evaluated with respect to real-world data collected using Smartphone-based vehicular sensing model. The proposed method is tested when measurement noises are both Gaussian and non-Gaussian distributed and also compared with the existing prediction methods. Experimental results confirm that B-SRGP presents higher accuracy prediction and lower mean-squared prediction error for vehicle position when measurement noises are non-Gaussian distributed.
Highlights
Nowadays, vehicle trajectory prediction is one of the major problems in intelligent transportation systems (ITS) where many researchers are widely interested in reliable safety applications
We propose an approach based on Bayesian-Sparse Random Gaussian Prediction (B-Sparse Random Gaussian Prediction (SRGP)) for vehicle position prediction
The originality lies in the use of a Sparse Random Gaussian matrix as a matrix measurement for vehicle position prediction as well as the sensors integrated in Smartphone to collect the dataset in urban city where free and natural partial global positioning system (GPS) outages were detected and full GPS
Summary
Vehicle trajectory prediction is one of the major problems in intelligent transportation systems (ITS) where many researchers are widely interested in reliable safety applications. Boucher and Noyer [1] introduced the hybrid method based on the combination of KF and particle filter (PF) to handle the partial GPS outages In their study, they mentioned that when GPS fails, the filter fuses all available pseudorange measures to improve the vehicle positioning. The main objective of this study is to develop a prediction approach-based lowcost GPS/INS model integrated in mobile computing device taking into account the challenging environments and providing a better vehicle positioning accuracy even during the full and partial GPS outages. When the GPS measurements are available but with low weight, that is, when the number of satellites in view is less than four, the partial GPS outage is detected In this case, the Bayesian inference is applied to SRGP method to model the inputs from both the INS and GPS measurements in order to provide the reliable prediction accuracy.
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