The paper deals with an integrated automation system concept for conflict detection and resolution during airport surface operations. The integration exercise features ground-side and flight-deck-side automation systems. In addition to the conflict detection and resolution subsystems , the integration also considers the planner automation subsystem on the ground side. Integration is facilitated through information exchange over a datalink between the tower and the flight deck. The paper presents three different options of integration: (i) Option A representing an "Alerts Exchanged" mode in which only conflict-related information is exchanged, (ii) Option B representing an "Intent Exchanged" mode in which intent information is exchanged, and (iii) Option C representing a "Tightly Integrated" mode which is a blend of Option A and Option B integration concepts. The paper also lists out the datalink communication requirements and the automation functional requirements for each of these concepts. The benefits of integration are illustrated using some examples.
The paper deals with the concept and requirement for airport surface Conflict Detection and Resolution (CD&R). The scope of the proposed CD&R concept spans across three different timeframes: (i) near-term (2015), (ii) mid-term (2020), and (iii) far-term (2025). Enabling technologies such as (i) surveillance, (ii) airport surface operations planning automation, (iii) clearance delivery mechanism, (iv) clearance information available to CD&R automation, and (v) flight-deck automation are studied. The paper identifies the functional requirements for the CD&R automation system such as aircraft state estimation module and aircraft trajectory prediction module. Detalied descriptions of the individual algorithms are beyond the scope of the current paper and will be presented in a future paper. However, preliminary closed-loop simulation results obtained with the conflict detection and resolution system are presented. I. Introduction urrent-day operations require the Air Navigation Service Provider (ANSP) to specify the taxi routes, control the order of merging at intersections, sequence runway crossings and departures at the runways, and require the pilots to provide separation visually. To enhance situational awareness of the ANSP, the FAA is introducing new surface surveillance technologies such as Airport Surface Detection Equipment-Model X (ASDE-X) 1 and Automatic Dependent Surveillance-Broadcast (ADS-B) 2 , which provide aircraft position data in all-weather situations and support the prediction of future aircraft trajectories more accurately than before. Other technologies useful for conflict and incursion detection or prevention include the Airport Movement Area Safety System (AMASS) 3,4 and Runway Status Lights 5. Previous NASA research for improving situational awareness on the flight deck include the Taxiway Navigation and Situation Awareness (T-NASA) System 6,7 developed at NASA Ames Research Center, and the Runway Incursion Prevention System (RIPS) 8,9 developed at NASA Langley Research Center. Researchers at NASA Langley are also building on the earlier RIPS technologies to develop flight-deck technologies for collision avoidance 10 referred to as Collision Avoidance for Airport Traffic (CAAT). The Runway Incursion Alerting System (RIAS) 11 consisting of millimeter-wave radar and pan/tilt/zoom cameras was developed by QinetiQ. The Surface Management System (SMS) 12 , developed by NASA in cooperation with the FAA, is a valuable decision-support tool for service providers and users of the National Airspace System (NAS) for providing situational awareness of the airport traffic 13. Researchers from Mosaic ATM used the route generation capability of the Surface Decision Support System (SDSS)-the SMS testbed fielded by the FAA-to study the feasibility of a conformance monitoring function 14. Mosaic ATM is currently investigating surface trajectory prediction and taxi conformance monitoring under a NASA Research Announcement (NRA) award 15. The EUROCONTROL Advanced Surface Movement Guidance and Control System (A-...
The paper deals with the development a ground-side conflict detection automation system for NextGen airport surface operations. The automation system is referred to as "Monitor Airport Environment: Surface Traffic and Runway Operations (MAESTRO)." In contrast to current-day conflict detection systems, MAESTRO has been designed taking into account NextGen operational concepts from mid-term and far-term timeframes. Conflicts of interest are Taxiway Collisions and Runway Incursions. A new conflict alert referred to as "Runway Incursion Situation Alert (RISA)" is created to actively prevent runway incursions. The automation system is driven by surveillance inputs and the outputs from airport planning systems such as Spot and Runway Departure Advisor (SARDA). MAESTRO consists of three modules: (i) Trajectory Prediction module, (ii) Conflict Detection module, and (iii) Controller Display module. The trajectory prediction module generates the 4D-trajectory predictions along with their uncertainty estimates. The paper develops the framework for both deterministic and probabilistic conflict detection. MEASTRO has been tested using actual surface traffic data from Dallas/Fort Worth International Airport (DFW). The evaluations indicate promising performance with zero missed-alerts and few false alarms that are actually close encounters. It is shown that situations which could potentially become Runway Incursions could be detected as RISAs with a lead-time of 60 seconds.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.