Detect-and-Avoid Closed-Loop Evaluation of Noncooperative Well Clear Definitions

Wu, Minghong G.; Lee, Seungman; Serres, Christine; Gill, Bilal; Edwards, Matthew; Smearcheck, Samantha; Adami, Tony; Calhoun, Sean

doi:10.2514/1.d0199

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Majority of encounters had a HMD and VMD greater than 2200 and 450 feet, respectively, at CPA. This combination of HMD and VMD are notable as it a separation metric used by RTCA SC-228 [27][28][29] and similar to separation criteria used by a different DAA standard published by ASTM F38 [30]. As the OpenSky Network-based model had 25% of encounters with a HMD and VMD of this or less, that encounter set was slightly more stressing to the DAA system and had encounters to estimate safety metrics based on that separation.…”

Section: Horizontal and Vertical Miss Distance Distributionsmentioning

confidence: 89%

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

et al. 2022

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The integration of new airspace entrants into terminal operations requires design and evaluation of Detect and Avoid systems that prevent loss of well clear from and collision with other aircraft. Prior to standardization or deployment, an analysis of the safety performance of those systems is required. This type of analysis has typically been conducted by Monte Carlo simulation with synthetic, statistically representative encounters between aircraft drawn from an appropriate encounter model. While existing encounter models include terminal airspace classes, none explicitly represents the structure expected while engaged in terminal operations, e.g., aircraft in a traffic pattern. The work described herein is an initial model of such operations where an aircraft landing or taking off via a straight trajectory encounters another aircraft landing or taking off, or transiting by any means. The model shares the Bayesian network foundation of other Massachusetts Institute of Technology Lincoln Laboratory encounter models but tailors those networks to address structured terminal operations, i.e., correlations between trajectories and the airfield and each other. This initial model release is intended to elicit feedback from the standards-writing community.

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Section: Horizontal and Vertical Miss Distance Distributionsmentioning

confidence: 89%

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

et al. 2022

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“…As UAS applications continue to expand, researches have been carried out on the path prediction, tracking and control as well as collision avoidance algorithm development [27,28,29,30,31,32,33,34]. Detect-and-Avoid concept of operation is illustrated in [35] and several studies [36,37,38,29,39] specifically focus on the safety critical analysis of a standalone DAA with different methodologies as well as design of cognitive pilot-machine interface [40,41]. When implementing a shared architecture with UAS DAA, it is important to consider any inconsistency or latency that exists in the communication channel, since communication is crucial for sending surveillance information to ground control station and receiving pilot command.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

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This paper develops and evaluates the performance of an allocation agent to be potentially integrated into the onboard Detect and Avoid (DAA) computer of an Unmanned Aircraft System (UAS). We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot. With a communication channel prone to latency, we consider a mixed initiative interaction environment, where the control authority of the UAS is dynamically allocated by the allocation agent. In an encounter with a dynamic intruder, the probability of collision may increase in the absence of pilot commands in the presence of latency. Moreover, a delayed pilot command may not result in safe resolution of the current scenario and need to be improvised. We design an optimization algorithm to reduce collision risk and refine delayed pilot commands. Towards this end, a Markov Decision Process (MDP) and its solution are employed to create a wait time map. The map consists of estimated times that the UAS can wait for the remote pilot commands at each state. A command blending algorithm is designed to select an avoidance maneuver that prioritizes the pilot intention extracted from the pilot commands. The wait time map and the command blending algorithm are implemented and integrated into a closed-loop simulator. We conduct ten thousands fast-time Monte Carlo simulations and compare the performance of the integrated setup with a standalone DAA setup. The simulation results show that the allocation agent enables the UAS to wait without inducing any near mid air collision (NMAC) and severe loss of well clear (LoWC) while positively improve pilot involvement in the encounter resolution.

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“…Wu et al and Lee et al evaluated the definitions of well-clear. 7,8) They simulated numerous encounters using the uncorrelated encounter model developed by the Massachusetts Institute of Technology Lincoln Laboratory. 9) Another research topic is to develop an approximate model of ACAS Xu using neural network compression, 10,11) which can reduce the amount of data storage space required by ACAS Xu for RPAS.…”

Section: Introductionmentioning

confidence: 99%

Potential Operational Consequences of the Use of ACAS Xu in Controlled Airspace

AVENEAU,

TORATANI,

SENOGUCHI

et al. 2024

Trans. Japan Soc. Aero. S Sci.

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A "detect-and-avoid" capability providing a "remain well-clear" function will be needed for unmanned aircraft to fly safely in uncontrolled airspace. It could also be used in controlled airspace provided that the action chosen by the pilot, based on the system suggestive guidance, is compatible with the current air traffic control clearance or an amended clearance. The study reported in this paper looks at the potential operational consequences of the interaction between a pilot and the air traffic controller in controlled airspace when a clearance amendment must be requested by the pilot using the recently standardized detect-and-avoid system, Airborne Collision Avoidance Systems Xu (ACAS Xu). Numerical simulations on a potential encounter are performed. A model of pilot behavior when faced with a remain well-clear alert (including pilot-controller communication delays) is used. An analysis of the qualitative results outlines several areas of concern: the possible confusion caused by multiple changes in remain well-clear guidance, the undesirable effects when both aircraft are equipped with ACAS Xu, and the potential interaction with short-term conflict alerts displayed at the air traffic controller's workstation.

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Detect-and-Avoid Closed-Loop Evaluation of Noncooperative Well Clear Definitions

Cited by 4 publications

References 8 publications

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Potential Operational Consequences of the Use of ACAS Xu in Controlled Airspace

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