Moving Horizon Estimation With Variable Structure Interacting Multiple Model for Surrounding Vehicle States in Complex Environments

• Published in: IEEE transactions on intelligent transportation systems 2024-12, Vol.25 (12), p.19943-19961
• Main Authors: Lu, Haikuan, Wang, Ping, Qu, Ting, Chen, Hong, Zhang, Lin, Hu, Yunfeng
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptation models; Computational modeling; Estimation; interacting multiple model Kalman filter; Kalman filters; motion prediction; moving horizon estimation; Prediction algorithms; Predictive models; Surrounding vehicle state estimation; Time-domain analysis; Trajectory; variable structure multiple model estimation; Vehicular ad hoc networks
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2024.3467042

Motion prediction of surrounding vehicles in complex environments is essential for autonomous vehicle trajectory planning. Accurate motion prediction requires accurately estimating the state information of the surrounding vehicles. For this purpose, a moving horizon estimation with interacting multiple model (IMM-MHE) algorithm is first proposed here. The algorithm can match multiple vehicle maneuvers, but also fully utilizes the historical information obtained during the driving process, achieving a high estimation accuracy. Second, a moving horizon estimation with variable structure interacting multiple model (VSIMM-MHE) framework is designed. Time-domain adaptation is proposed to solve the problem that the fixed time domain of some models cannot be filled due to model activation and elimination. A new interaction method is proposed to solve the problem that models cannot interact because the starting timesteps of their time domains are different. The proposed framework reduces not only the computational burden, but also the final estimation error caused by the model not matching the current maneuver. Third, based on a model set consisting of different kinds of intention models, a VSIMM-MHE algorithm is proposed. This algorithm introduces residual information into the model classification method, reducing the dependence on the accuracy of the model probabilities. It can not only accurately estimate the state information of surrounding vehicles in a complex environment, but also identify the model that best matches the current maneuver and effectively predict the motion trajectories of surrounding vehicles through model probabilities. Finally, joint simulation with SCANeR studio, Carsim and Simulink and hardware-in-the-loop experiment demonstrate the effectiveness of not only the two proposed estimation algorithms but also the motion prediction of surrounding vehicles using the model probabilities in the VSIMM-MHE algorithm.