Representing dynamic environments for autonomous ground vehicle navigation

For an autonomous vehicle to drive in a dynamic environment, it must be able to detect moving objects, predict their future location, represent this information in its internal knowledge bases, and make appropriate plans based upon all of this information. The estimation of what the object is and where it is expected to be in the future must be continuously refined as time progresses. This paper discusses a multi-representational approach to capturing important characteristics about moving objects in a dynamic environment. Specifically we look at applying a combination of symbolic, equation-based, and grid-based representations to fully represent the information that is needed for different types of planners to develop appropriate plans in the presence of moving objects.