Decision Making Improvement Based on the Ahead Traffic Flow During the Lane Change Manoeuvre Via Model Predictive Control

This paper proposes a novel decision-making framework that combines the influence of ahead traffic flow with the driver's personal decisions, thereby addressing the impact of transient traffic flow on lane-change decision-making. The presented algorithm can design safe trajectories without any collisions at any time of the manoeuvre considering the effects of ahead traffic flow on future decisions of the surrounding vehicles and sudden independent decisions of the surrounding vehicles during the lane change manoeuvre. In order to combine the microscopic and macroscopic models of the traffic environment around the ego vehicle, the ahead traffic flow is modelled and it is combined with the independent movements of the front vehicle in the target lane that is due to the driver's personal decisions. Using the model-based predictive control, the effects of these changes are investigated during the lane change manoeuvre. The algorithm successfully completed all lane change manoeuvres with collision avoidance considering the changes in surrounding vehicles caused by the ahead traffic flow. The performance of the proposed algorithm is simulated in complicated lane change manoeuvre regarding transient changes in the traffic flow and it is validated in IPG Automotive (IPG CarMaker) dynamic environment considering surrounding vehicles. The results indicate the desired performance of the proposed algorithm regarding macroscopic and microscopic changes around the ego vehicle even during the lane change manoeuvre.