This paper presents a hierarchical hybrid predictive control framework for an autonomously controlled road vehicle. At the top, an assigner module is designed as a finite state machine for decision-making. Based on the current information of the controlled vehicle and its environment (obstacles, and lane markings, etc), the assigner selects discrete maneuver states through pre-defined switching rules. The several maneuver states are related to different setups for the underlying model predictive trajectory guidance module. The guidance module uses a reduced-order curvilinear particle motion description of the controlled vehicle and obstacle objects as well as a corresponding description of the reference path, lane and traffic limits. The output of the guidance module interfaces with the lower level controller of the continuous vehicle dynamics. The performance of the proposed framework is demonstrated via simulations of highway-driving scenarios.

This content is only available via PDF.
You do not currently have access to this content.