A first-order generalized pseudo-Bayesian method based on moving horizon estimation for surrounding vehicle states estimation in complex environments

Abstract

Predicting the motion of surrounding vehicles is important for autonomous vehicle trajectory planning. To ensure the accuracy of motion prediction, it is particularly important to obtain information about the state of surrounding vehicles at the current moment. For this purpose, a first-order generalized pseudo-Bayesian method based on moving horizon estimation (GPB1-MHE) is proposed here. First, for vehicles, pedestrians and obstacles in complex environments, we design several different intention-based motion models to describe multiple maneuvers of vehicles during driving. Second, we replace the Kalman filter (KF) in the first-order generalized pseudo-Bayesian (GPB1) method with moving horizon estimation (MHE) to obtain GPB1-MHE. Compared with GPB1, it makes full use of the historical state information of a vehicle to improve the estimation accuracy. Finally, the effectiveness of the estimation method is verified using four operating conditions, overtaking, overtaken, pedestrian approaching, and pedestrian staying away, in a SCANeR studio and Simulink joint simulation environment.