Interaction-Aware Traffic Prediction and Scenario-Based Model Predictive Control for Autonomous Vehicles on Highways

Abstract

This article addresses the problem of traffic prediction and control of autonomous vehicles on highways. An interacting multiple model Kalman filter (IMM-KF)-related algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenarios of the vehicles. A novel integrated decision-making and control system is proposed by applying a scenario-based model predictive control (MPC) approach. The designed controller considers safety, driving comfort, and traffic rules. The recursive feasibility of the controller is guaranteed under the inclusion of the “worst case” as an additional scenario to obtain safe inputs. Finally, the proposed scheme is evaluated under a high-fidelity IPG CarMaker and Simulink co-simulation environment. Simulation results indicate that the vehicle performs safe maneuvers under the designed control framework in different traffic situations.