Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers
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Abstract (arXiv)
Aircraft collision avoidance systems have long been a key factor in keeping our airspace safe. Over the past decade, the FAA has supported the development of a new family of collision avoidance systems called the Airborne Collision Avoidance System X (ACAS X), which model the collision avoidance problem as a Markov decision process (MDP). Variants of ACAS X have been created for both manned (ACAS Xa) and unmanned aircraft (ACAS Xu and ACAS sXu). The variants primarily differ in the types of collision avoidance maneuvers they issue. For example, ACAS Xa issues vertical collision avoidance advisories, while ACAS Xu and ACAS sXu allow for horizontal advisories due to reduced aircraft performance capabilities. Currently, a new variant of ACAS X, called ACAS Xr, is being developed to provide collision avoidance capability to rotorcraft and Advanced Air Mobility (AAM) vehicles. Due to the desire to minimize deviation from the prescribed flight path of these aircraft, speed adjustments have been proposed as a potential collision avoidance maneuver for aircraft using ACAS Xr. In this work, we investigate the effect of speed change advisories on the safety and operational efficiency of collision avoidance systems. We develop an MDP-based collision avoidance logic that issues speed advisories and compare its performance to that of horizontal and vertical logics through Monte Carlo simulation on existing airspace encounter models. Our results show that while speed advisories are able to reduce collision risk, they are neither as safe nor as efficient as their horizontal and vertical counterparts.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 29 Apr 2022.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 1.
6 papers.
- 2023Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconflictioncited by 6
- 2022AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobilitycited by 4
- 2023AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulatorcited by 4
- 2023Towards a Standardized Reinforcement Learning Framework for AAM Contingency Managementcited by 1
- 2024Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobilitycited by 0
- 2025Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptancecited by 0
- 2025Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptance23 Oct 2025
- 2024Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility28 Jun 2024
- 2023Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management17 Nov 2023
- 2023AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator19 Jun 2023
- 2023Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction17 May 2023
- 2022AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility9 Jun 2022