Key points are not available for this paper at this time.
This paper describes a world model that combines a variety of sensed inputs and a priori information and is used to generate on-road and off-road autonomous driving behaviors. The system is designed in accordance with the principles of the 4D/RCS architecture. The world model is hierarchical, with the resolution and scope at each level designed to minimize computational resource requirements and to support planning functions for that level of the control hierarchy. The sensory processing system that populates the world model fuses inputs from multiple sensors and extracts feature information, such as terrain elevation, cover, road edges, and obstacles. Feature information from digital maps, such as road networks, elevation, and hydrology, is also incorporated into this rich world model. The various features are maintained in different layers that are registered together to provide maximum flexibility in generation of vehicle plans depending on mission requirements. The paper includes discussion of how the maps are built and how the objects and features of the world are represented. Functions for maintaining the world model are discussed. The world model described herein is being developed for the Army Research Laboratory's Demo III Autonomous Scout Vehicle experiment.
Hong et al. (Wed,) studied this question.