A Reliable Vehicle Lateral Velocity Estimation Methodology Based on SBI-LSTM During GPS-Outage

Abstract

Vehicle lateral velocity is critical for the high-level automatic driving technology, andyyfusing Global Position System (GPS) in the lateral velocity estimation method can greatly improve the estimation accuracy. However, the method fusing GPS is seldom reported, since the problem of GPS-outage often appears. Accordingly, this article proposes a novel lateral velocity estimation method (VLVEM) based on SBI-LSTM in GPS-outage environment. Additionally, VLVEM integrating Inertial Navigation System-aided GPS (INS-aided GPS) is derived from Federated Kalman Filter (FKF) algorithm. Furthermore, during GPS-outage, induced from the Stack Bidirectional Long Short-Term Memory Recurrent Neural Network (SBI-LSTM RNN), an INS-aided GPS fault reconstructor (IGFR) is designed to reconstruct INS-aided GPS model. Finally, the simulation results show that compared with most of the existing methods which only consider the in-vehicle sensors’ signals, the proposed method fusing GPS has higher lateral velocity estimation accuracy. Besides, when GPS-outage causes INS-aided GPS failure, IGFR can reconstruct INS-aided GPS model, and VLVEM still has high estimation accuracy. Combining the in-vehicle sensors and GPS, VLVEM exhibits great robustness and fault tolerance.