An Interacting Multiple Model for Trajectory Prediction of Intelligent Vehicles in Typical Road Traffic Scenario

Hongbo Gao, Yechen Qin, Chuan Hu, Yuchao Liu, Keqiang Li

2021IEEE Transactions on Neural Networks and Learning Systems81 citationsDOI

Abstract

This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.

Topics & Concepts

TrajectoryKinematicsComputer scienceHidden Markov modelIntelligent transportation systemArtificial intelligenceMarkov processVehicle dynamicsMarkov chainMarkov modelKey (lock)Control theory (sociology)SimulationEngineeringArtificial neural networkControl engineeringAutonomous Vehicle Technology and SafetyTraffic Prediction and Management TechniquesTraffic control and management