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NASA has developed the Autonomous Operations Planner (AOP) airborne decision support tool to explore advanced air traffic control concepts that include delegating separation authority to aircraft. A key element of the AOP is its strategic conflict resolution (CR) algorithm, which must resolve conflicts while maintaining conformance with traffic flow management constraints. While a previous CR algorithm, which focused on broader flight plan optimization objectives as a part of conflict resolution, had successfully been developed, new research has identified the need for resolution routes the users find more acceptable (i.e., simpler and more intuitive). A new CR algorithm is presented that uses a combination of pattern-based maneuvers and a genetic algorithm to achieve these new objectives. Several lateral and vertical maneuver patterns are defined and the application of the genetic algorithm explained. A new approach to defining a conflicted fitness function using estimates of the local conflict region around a conflicted trajectory is also presented. Preliminary performance characteristics of the implemented algorithm are provided.

A methodology and algorithms for the detection of divergences of aircraft from their flight plans on the horizontal plane are presented. Deviations from the flight plan are often the result human error, such as miscommunication between the pilot and the air traffic controller (ATC), situational awareness errors of the ATC, discrepancies between the mental picture of the ATC and his/her decision support tools, etc. The goal is to detect divergences due to these factors as early as possible and provide a timely warning. The difficulty is distinguishing these “important” divergences from generic deviations, due for example to local wind conditions. Here we develop algorithms for addressing this problem, inspired by methods from fault detection and isolation. The algorithms are tested on a detailed simulation of a Boeing 763-300 flying in a wind field with realistic spatio-temporal correlation structure.

In today’s air traffic management system, the intent of an aircraft is revealed in its flight plan and a pilot’s adherence to published navigation routes. In a future Free Flight environment, aircraft may be allowed to fly any route they choose. Intent will likely be broadcast in the form of Trajectory Change Points (TCPs), for instance, up to four TCPs in an Automatic Dependent Surveillance – Broadcast (ADS-B) message. However, if TCPs do not accurately represent intent, do not exist or do not get received, then a nearby aircraft or ground monitoring system has a need to infer the pilot’s intent. In this paper, a method of inferring intent is investigated which is based on artificial intelligence models and a process for best fitting an intent model to observed aircraft motion. Horizontal, vertical, and speed dimensions are first investigated independently, and then combined, including sequences of actions, to fully explain the 3-dimensional guidance and navigation plan of an aircraft. Finally, the inferred intent is used as a basis to predict a path from the current location of the aircraft into the future.

When using recorded air traffic data to measure the accuracy of air traffic management decision support tools that use intent-based trajectory modelers, it is necessary to determine whether or not an aircraft is adhering to its known flight plan clearance. This paper defines what is meant by adherence and presents metrics that can be used to define lateral flight adherence. The paper describes an algorithm that is currently being applied that uses a subset of these metrics. The paper then presents a number of examples obtained from recorded air traffic data, which show instances where aircraft deviate from their known lateral clearance. The paper then presents a number of alternative approaches that could be used to implement a better algorithm for determining whether or not an aircraft was in lateral adherence based on recorded air traffic data.

Air traffic control operations would require advanced methods for aircraft tracking, intent inference and trajectory prediction. These methods are useful to increase the capacity of the National Airspace System (NAS), without sacrificing its safety. In this paper we propose an algorithm that performs these three tasks accurately. The algorithm utilizes a hybrid estimation algorithm to estimate the aircraft’s state and flight mode. These estimated quantities are then combined with knowledge about the flight plan, the environment and ATC regulations, to estimate the pilot’s intent. Trajectory predictions are computed using the aircraft motion (state and mode estimates) and the inferred intent. The result is an algorithm that provides, in real-time, accurate intent and trajectory predictions for aircraft.

The autonomous flight control methodology for the Unmanned Aerial Vehicle, applying the Fuzzy Expert Control Concept is discussed. When it comes to the problem of co-existence with the Manned Aircraft in the National Air Space for the UAV, The UAV for the NAS flight is required to carry the same level of control laws and systems integrity as those for the Manned Aircraft. As for the autonomous traffic control in NAS flight, the Fuzzy Expert Concept is applied to control as a core law for both of the steps. First is a decision making step for a change of the flight plan meeting with the needs in the actual traffic situations for the avoidance from intruders, or from an uneasy weather area for example. Second is an action step to carry out the altered flight plan until the point, from where the Flight Management System of the vehicle resumes the control. Those are illustrated with some MATLAB simulation results. The Fuzzy Expert Control Concept is effective and useful, because the conformation of the laws is very practical basing on the Expert Concept, compensating the defects of the Expert System by taking the fussiness of the conditions into consideration to complete the autonomous control, with no leaning time. The UAV for the NAS flight is also referred to have the same level of reliability and safety as the one for the civil transports, with the certification of the vehicle and the qualified ground pilot.

The data needed for air traffic flow management decision support tools is provided by the Enhanced Traffic Management System (ETMS). This includes both the tools that are in current use and the ones being developed for future deployment. Since the quality of decision support provided by all these tools will be influenced by the quality of the input ETMS data, an assessment of ETMS data quality is needed. Motivated by this desire, ETMS data quality is examined in this paper in terms of the unavailability of flight plans, deviation from the filed flight plans, departure delays, altitude errors and track data drops. Although many of these data quality issues are not new, little is known about their extent. A goal of this paper is to document the magnitude of data quality issues supported by numerical analysis of ETMS data. Guided by this goal, ETMS data for a 24-hour period were processed to determine the number of aircraft with missing flight plan messages at any given instant of time. Results are presented for aircraft above 18,000 feet altitude and also at all altitudes. Since deviation from filed flight plan is also a major cause of trajectory-modeling errors, statistics of deviations are presented. Errors in proposed departure times and ETMS-generated vertical profiles are also shown. A method for conditioning the vertical profiles for improving demand prediction accuracy is described. Graphs of actual sector counts obtained using these vertical profiles are compared with those obtained using the Host data for sectors in the Fort Worth Center to demonstrate the benefit of preprocessing. Finally, results are presented to quantify the extent of data drops. A method for propagating track positions during ETMS data drops is also described.

In recent years, much attention has been focused on emerging concepts of decentralized control for the National Airspace System. The introduction of a distributed decision-making environment in a future air traffic management system gives airspace users (pilots and airline operations centers) freedom to change and optimize flight plans in real time, in contrast to the current operational paradigm characterized by centralized decision-making by the air traffic service provider. Such an approach to air traffic operations will require individual decision-makers within the airspace to identify and solve, in real time, routing problems to avoid traffic conflicts, weather cells and Special Use Airspace. In this paper, we apply a modeling and simulation based approach to investigate the potential for a Principled Negotiation-based approach to the distributed air traffic management problem. Another objective is to identify the frequency with which truly collaborative (i.e., negotiation) behavior may be required to solve air traffic conflicts while avoiding regions of airspace. A free flight simulation in Los Angeles Center airspace, based on real traffic and Special Use Airspace data indicates that over 8% of aircraft-to-aircraft conflicts require negotiation. Principal Scientist; kharper@cra.com. Member, AIAA. Author to whom all correspondence should be addressed. ✝ Software Engineer. Member, AIAA. ‡ Software Engineer. § Senior Scientist. Member, AIAA Research Scientist, Automation Concepts Research Branch; Mail Stop 210-10; E-mail: kbilimoria@mail.arc.nasa.gov. Associate Fellow, AIAA. ✝✝ Software Engineer, Raytheon ITSS. Introduction Emerging concepts for future air traffic management (ATM) systems and procedures will dramatically change human roles and tasks in the National Airspace System (NAS). There are several technical initiatives in the ATM community for the development of new concepts of operations to meet the projected airspace demands of the future, while maintaining the overall safety of the NAS. The Free Flight paradigm (RTCA, 1995) gives aircraft operators the freedom to optimize their trajectories in real time, while requiring them to assume responsibility for maintaining safe separation from other aircraft and conforming to any ATM restrictions imposed by the air traffic service provider. One possible approach to implement Free Flight is the Distributed Air/Ground Traffic Management (DAG-TM) concept of operations (NASA, 1999). DAG-TM is characterized by distributed decision-making among pilots, air traffic service providers and airline operational control (AOC) personnel, who work cooperatively to optimize both individual and global operations, while maintaining safety as the highest priority. The introduction of this envisioned distributed decision-making (DDM) environment, however, will have profound implications on pilot/controller/AOC information requirements, roles and responsibilities, allocation of workload, communication, and decisionmaking throughout the ATM system. In order to evaluate the feasibility of such ATM concepts, there is a need to model the new rules and protocols governing individual behavior of the key decision-makers in the air transportation system, including pilots, air traffic controllers and airline dispatchers. Because these decision-makers will ultimately drive overall ATM system performance and safety, any modeling and simulation approach to system analysis must include realistic human behavior representations (HBRs) of the key decision-makers. Furthermore, if we are to capture AIAA Guidance, Navigation, and Control Conference and Exhibit 5-8 August 2002, Monterey, California AIAA 2002-4552 Copyright © 2002 by the author(s). Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. 2 American Institute of Aeronautics and Astronautics realistic behaviors in the DDM environment, these HBRs must incorporate the underlying perceptual and cognitive capabilities and limitations that determine decisions made by these key players. To support these objectives, this work has focused on the development of an agent-based modeling approach of human behavior, which allows us to investigate the feasibility of distributed control through principled negotiation (Jacolin and Stengel, 1998; Wangermann and Stengel, 1996) in the air traffic environment, and provides a test bed for new communications protocols, information requirements, and traffic management models. The principled negotiation approach to distributed decision-making involves the search for solutions that provide the greatest mutual gains with respect to each participating individual’s requirements. Following from previous efforts (Harper et al., 1998; Harper et al., 1999), we have furthered the development of agent-based models of pilot, air traffic controller, and airline dispatcher behavior. The resulting agents are responsible for collaborating in a simulated advanced ATM environment to resolve traffic conflicts and avoid regions of airspace, e.g., convective weather cells or Special Use Airspace (SUA), in a safe, fair and timely manner. Under previous development efforts (Harper et al., 1999), we have demonstrated the capability of the agent-based models of collaborative decision-making to identify specific problems and solve them through democratic negotiation. In this paper, we are interested in investigating the actual requirements for truly collaborative decision-making in the ATM environment. Algorithmic conflict resolution and rerouting methods have been developed and demonstrated in the literature, and could be implemented within a real-time autonomous route planning and adjustment tool (Bilimoria, 2000; Krozel et al., 1996; Tomlin and Pappas, 1998). The question now becomes whether such fully decentralized methods are sufficient to support separation assurance and hazard avoidance, or if more complex distributed control strategies are required to maintain the efficiency and safety of the NAS. The paper is outlined as follows. First, we provide the technical background underlying the development of our agent-based models of human decision-making and collaboration through principled negotiation. We then present an overview of the knowledge engineering (KE) based model development process employed in the construction of agent-based representations of commercial pilots, air traffic controllers, and AOC dispatchers, followed by a summary of the results of the KE efforts. This discussion concludes in a description of the information processing, situation assessment, and collaborative decision-making functions developed across the three targeted agent models. We then present an overview of the integrated system architecture that brings these models together within the simulated air traffic environment provided by the Future ATM Concepts Evaluation Tool (FACET) (Bilimoria et al., 2001). To support the experimental objective of this paper, we then describe a simulated air traffic scenario that incorporates realistic traffic and SUA data for Los Angeles Center (ZLA). We populate this scenario with dynamic agent representations and analyze their collaborative problem-solving behavior to determine the level of complexity that can be expected in a distributed ATM environment, and the frequency and nature with which flight plan amendments must be truly negotiated among multiple decision-makers within the system. Finally, we present some conclusions. Human Behavior Representation Developing agent models of collaborative decisionmaking behavior among commercial pilots, air traffic controllers and AOC dispatchers are based on SAMPLE (Situation Assessment Model of Pilot-in-theLoop Evaluation), an agent-based human behavior modeling architecture originally developed to model fighter pilots, but designed within a domainindependent architecture. SAMPLE is based on a hierarchical definition of behavior, where the highest levels represent ongoing cognitive tasks and the lowest levels define specific information processing, situation assessment, and procedurally-driven decision-making models. We believe that SAMPLE provides a strong agent-based modeling approach for advanced ATM environments, supporting the modular development of various agent representations. SAMPLE Overview The SAMPLE human behavior model is based on the Rasmussen Hierarchy of human information processing and skilled behavior (Rasmussen, 1983), which provides a strong unifying theoretical framework for analysis of different human skills that may be modeled within a real-time simulation. By dividing skilled behavior into categories based on the degree of automaticity, complexity, and level of cognitive processing, this framework supports systematic skill decomposition and measurement of individual aspects of the overall skill on a part-task basis.

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

In today's air traffic control system, a level of intent of an aircraft is revealed in it's flight plan and a pilot's adherence to published navigation routes. In the future Free Flight environment, aircraft may be allowed to fly any route they choose. Intent will likely be broadcast in the form of Trajectory Change Points (TCPs), for instance, the next two TCPs called TCP and TCP+1 in an Automatic Dependent Surveillance - Broadcast (ADS-B) message. However, if TCPs do not accurately represent intent, do not get received, or do not exist, then an aircraft has a need to infer the intent of another aircraft. In this paper, a method of inferring intent is investigated which is based on artificial intelligence models of actions and a process for best fitting an intent model to observed actions of an aircraft. With such an intent inference algorithm, the TCPs for an aircraft can be inferred and used when the TCP data in a datalink message cannot be validated. While intent inference may not be needed in a normal situation, its use may enrich the presentation of data to the flight crew in a Free Flight environment.

Conflict detection and resolution (CD&R) systems are envisioned as an enabling technology for improving the efficiency, capacity, and safety of Air Traffic Management. The concept includes airborne equipment that monitors the traffic situation (conflict detection), informs pilots of potential airspace conflicts (conflict alerting), and suggests flight plan modifications to resolve the conflicts (conflict resolution.) This new, airborne surveillance and separation assurance function is made possible by Automatic Dependent Surveillance - Broadcast (ADS-B). One of the primary challenges for CD&R is the development of a conflict resolution algorithm that is both simple and robust. This paper contains results from recent tests of one such algorithm. The tests examined 1) the effects of delays in maneuver execution, 2) the problem of conflicts that occur near waypoints, and 3) the challenge of performing conflict resolution in the absence of intent information (i.e. knowledge of intended way point positions and arrival times). The first two studies confirmed the algorithm's robustness. The third study produced a surprising and exciting result - conflict resolution may be very successfully performed without knowledge of other aircrafts' intent information.