  This paper addresses the problem of traffic prediction and control of
autonomous vehicles on highways. A modified Interacting Multiple Model Kalman
filter 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 based on the predicted
information. A novel integrated decision-making and control system is proposed
by applying a Scenario-based Model Predictive Control 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 using the HighD dataset. Simulation results indicate that
the vehicle performs safe maneuvers in different traffic situations under the
designed control framework.
