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This paper addresses the problem of traffic prediction and control ofautonomous vehicles on highways. A modified Interacting Multiple Model Kalmanfilter algorithm is applied to predict the motion behavior of the trafficparticipants by considering their interactions. A scenario generation componentis used to produce plausible scenarios of the vehicles based on the predictedinformation. A novel integrated decision-making and control system is proposedby applying a Scenario-based Model Predictive Control approach. The designedcontroller considers safety, driving comfort, and traffic rules. The recursivefeasibility of the controller is guaranteed under the inclusion of the `worstcase' as an additional scenario to obtain safe inputs. Finally, the proposedscheme is evaluated using the HighD dataset. Simulation results indicate thatthe vehicle performs safe maneuvers in different traffic situations under thedesigned control framework.

Interaction-aware Traffic Prediction and Scenario-based Model Predictive Control for Autonomous Vehicles on Highways