- What is Aeronautical Information (AI)?
- AI Users
- AI System Qualities
- Current Challenges of AI Systems
- Shortcomings of Current AI Environment
- Requirements for an Improved AI Environment
What is Aeronautical Information?
Aeronautical Information (AI) is an overarching term that describes some of the critical information required for safe operation of the National Airspace System (NAS). Examples of AI include the following:
- Visualization and presentation of navigational and aeronautical data
- Radio aids to navigation (NAVAIDs) (frequencies, geospatial info)
- Airports Configuration (runways, taxiways, geospatial info, etc)
- Special Activity Airspace (SAA), Special Use Airspace (SUA), Memoranda of Agreement (MOAs), Temporary Flight Restrictions (TFRs)
- Notices to Airmen (NOTAMs)
- Surveys and Procedures
Aeronautical Information users include:
- Air Navigation Service Providers (ANSPs) (Air Traffic Controllers, Traffic Flow Managers, Supervisors)
- Flight Service Stations
- Airspace Users (Flight Operation Centers (FOCs), Aircrew, General Aviation and Business Aviation industries)
- Flight crew support and ATC Support (Traffic Flow Managers)
- Airport Operations
- Other government Entities (Department of Defense (DOD), Department of Homeland Security (DHS))
AI System Qualities
Due to the critical impact of AI on Airspace safety and flight operations, a mature AI system needs to be:
- Commonly represented
Current Challenges of AI Systems
Present day AI systems lack integration with each other, often presenting data in detached forms leaving it up to the user to integrate and interpret the discrete data cohesively. Such an isolated approach can no longer meet modern, flexible, and efficient mission requirements in the areas of safety, accuracy, timeliness, cost, and information technology. These systems provide the source data necessary for common situational awareness among users about flight constraints, airports, airspaces, and obstructions.
As a result, users of these systems do not have consistent access to authoritative NAS status information that could affect flights. Further, legacy AI systems are unable to meet Next Generation Air Transportation System's (NextGen) goals or international requirements and standards. These "stove-piped" systems are integrated point to point, which causes a lack of coordination and planning across a set of systems. This generates costly maintenance and extensions that require NAS users to rely on non-digital, unreliable, and inaccessible sources.
Shortcomings of Current AI Environment
- AI is inconsistent and costly to maintain across the current operational environment due to the duplicative functions and error-prone manual processing methods
- AI is still delivered on paper, through antiquated exchanges (phone, fax, email), and as - products (charts, reports, images) forcing work to be done at the consumer end to input, sort, filter, transform, and then use
- Current AI data types and visualizations are typically focused on a specific user group or data type (National Special Airspace Activity Program, Pilots' Bill of Rights)
- There are constant demands to improve AI quality, access, and consistency
Requirements for an Improved AI Environment
The challenges of the current environment inspired the need for a common, integrated, and accurate operational picture among NAS Stakeholders to support:
- Flight Planning, such as using SAA and NOTAM data to file most efficient trajectory
- Real-Time NAS Operations, such as notifying all stakeholders of a NAVAID outage via a NOTAM
- Traffic Flow Management, such as taking into account predicted SAA status when considering Traffic Flow Management Initiatives
- Post-Event Analysis, such as assessing use of airspace
NextGen needs fully digital data feeds to deliver smarter products, inputs for automated decision support, and filtered, sorted, tailored information to meet the future demands of NAS automation system users.