Online-Svr For Vehicular Position Prediction During Gps Outages Using Low-Cost Ins

Vehicle position prediction has become more and more critical for most applications in intelligent transportation systems (ITS). Prediction based INS/GPS integration provides continuous and reliable navigation solution when compared to standalone Inertial Navigation System (INS) or Global Positioning System (GPS). Although there have been several research works for fusing INS and GPS data to bridge navigation during GPS outages, most of them are offline methods and do not consider sensors data fluctuation due to traffic incident, inclement weather conditions or rush hour. This paper proposes a supervised statistical learning technique called Online Support Vector Machine for Regression (OL-SVR) for the prediction of vehicle position. During GPS availability, the OL-SVR models INS errors by fusing the INS and GPS data; meanwhile during outages, the trained OL-SVR method is utilized to predict accurate vehicle position. The proposed method is compared with two well-known prediction techniques including Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN). Experiments conducted at rush hour on real urban roads and simulation results prove that OL-SVR is more efficient and accurate in position prediction than PLSR and ANN, achieving an accuracy improvement of 20.3%-64.8%.