A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions

Trajectory prediction in autonomous driving system is an important aspect for preventing for instance the multi-vehicle collision. However, predicting accurately the future location of a vehicle is still a delicate task especially in intelligent transport systems. This paper proposes a hybrid approach of solving the position prediction problem of vehicle in multi-GPS outage conditions such as free and partial as well as short and long complete GPS outages. The proposed approach aggregates the advantages of both fuzzy inference system (FIS) and sparse random Gaussian (SRG) models, consequently named FIS-SRG, leading to a significant decrease in position prediction error of vehicle. The aforementioned outages are defined by adjusting the GPS propagation weight monitored by the Gaussian model and updated by fuzzy logic system. Experimental results based on data from GPS and INS and the comparison study with the existing prediction methods illustrate the good performance of the proposed approach, in all considered GPS outage conditions.
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