The advent of advanced computer architectures for parallel and symbolic processing has evolved technology to the point at which prototype autonomous vehicles are being developed. Control of such devices requires communication between knowledge-based subsystems in charge of the vision, planning, and control aspects necessary to make autonomous systems func-tional in a real-world environment. The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in dynamic situations, autonomous vehicles must be capable of interpreting terrain on the basis of predetermined mission goals. This paper describes an autonomous airborne-vehicle simulation currently being developed at the Georgia Tech Research Institute. The Autonomous Helicopter System (AHS) is a multimission system consisting of three distinct sections: vision, planning, and control. The vision section provides the local and global scene analyses that are symbolically represented and passed to the planning section as the initial route-planning constraints. The planning section generates a task-dependent path for the vehicle to traverse that assures maximum mission system successes as well as survivability. The control section validates the path and either executes the given route or feeds back to previous sections in order to resolve conflicts.