ACAS Xu provides both a collision avoidance (CA) and detect and avoid (DAA) capability for UAS. The CA decision logic is based on Markov decision process models of the CA problem to generate both horizontal and vertical maneuver guidance to avoid collisions. DAA logic uses a rollout approach to anticipate collision risk at longer timelines to support pilot interaction and communication with air traffic controllers. System optimization and evaluation is performed using fast-time Monte-Carlo simulation environments. Compared to benchmark systems, ACAS Xu provides a significant safety benefit and alerting reduction. The algorithm specification document for ACAS Xu is slated for final approval and release in 2020.

ACAS Xu: Integrated Collision Avoidance and Detect and Avoid Capability for UAS

Finding the most likely path to a set of failure states is important to the
analysis of safety-critical systems that operate over a sequence of time steps,
such as aircraft collision avoidance systems and autonomous cars. In many
applications such as autonomous driving, failures cannot be completely
eliminated due to the complex stochastic environment in which the system
operates. As a result, safety validation is not only concerned about whether a
failure can occur, but also discovering which failures are most likely to
occur. This article presents adaptive stress testing (AST), a framework for
finding the most likely path to a failure event in simulation. We consider a
general black box setting for partially observable and continuous-valued
systems operating in an environment with stochastic disturbances. We formulate
the problem as a Markov decision process and use reinforcement learning to
optimize it. The approach is simulation-based and does not require internal
knowledge of the system, making it suitable for black-box testing of large
systems. We present formulations for fully observable and partially observable
systems. In the latter case, we present a modified Monte Carlo tree search
algorithm that only requires access to the pseudorandom number generator of the
simulator to overcome partial observability. We also present an extension of
the framework, called differential adaptive stress testing (DAST), that can
find failures that occur in one system but not in another. This type of
differential analysis is useful in applications such as regression testing,
where we are concerned with finding areas of relative weakness compared to a
baseline. We demonstrate the effectiveness of the approach on an aircraft
collision avoidance application, where a prototype aircraft collision avoidance
system is stress tested to find the most likely scenarios of near mid-air
collision.

/pdf/adaptive-stress-testing-finding-failure-events-with-lvezhjzdjk.pdf

Adaptive Stress Testing: Finding Failure Events with Reinforcement Learning

Demand for small unmanned aircraft systems (sUAS) continues to increase in diversity and volume, however applications are currently limited by regulatory requirements for visual observation or special use waivers. For full integration of autonomous sUAS in the national airspace system, a collision avoidance system must be implemented to enable detection and avoidance of air traffic. Building upon collision avoidance systems development over the past decade for large UAS and manned aircraft, ACAS sXu provides such a capability, enabling autonomous and decentralized sUAS collision avoidance capability against manned aircraft, UAS, and other sUAS.

ACAS sXu: Robust Decentralized Detect and Avoid for Small Unmanned Aircraft Systems

This paper describes the implementation of four different collision avoidance algorithms designed for multi-rotor aircraft. The primary contribution of this paper is the evaluation of each using a common set of assumptions, simulation capabilities, and metrics. The first algorithm is an extension of the next-generation manned aircraft algorithm that will soon be deployed worldwide; it poses the collision avoidance problem as a partially observable Markov decision process and solves offline for the approximately optimal solution using dynamic programming. The second algorithm is based on receding-horizon model predictive control, and the third is based on potential field methods. The fourth algorithm is an adaptation of a tactical conflict detection and resolution algorithm that uses candidate trajectory predictions to determine a preferred resolution. A batch simulation system generates trajectories and computes aggregate metrics related to each algorithm’s performance, allowing direct comparison of the benefits and drawbacks of each approach. A second contribution of this paper is a description of the first known encounter model of hobbyist unmanned aircraft trajectories. This model is used to generate trajectories that are representative of the airspace. All algorithms have the flexibility to provide different tradeoffs between separation from an intruder and the trajectory deviation necessary to achieve that separation. The algorithm generated using dynamic programming delivers excellent maximum deviation performance with only slightly smaller separations than the model predictive control algorithm, and it does so with less than half of the required velocity change of the other algorithms.

Simulation Comparison of Collision Avoidance Algorithms for Small Multi-Rotor Aircraft

The capability to avoid other air traffic is a fundamental component of the layered conflict management system to ensure safe and efficient operations in the National Airspace System. The evaluation of systems designed to mitigate the risk of midair collisions of manned aircraft are based on large-scale modeling and simulation efforts and a quantitative volume defined as a near midair collision (NMAC). Since midair collisions are difficult to observe in simulation and are inherently rare events, basing evaluations on NMAC enables a more robust statistical analysis. However, an NMAC and its underlying assumptions for assessing close encounters with manned aircraft do not adequately consider the different characteristics of smaller UASonly encounters. The primary contribution of this paper is to explore quantitative criteria to use when simulating two or more smaller UASs in sufficiently close proximity that a midair collision might reasonably occur and without any mitigations to reduce the likelihood of a midair collision. The criteria assumes a historically motivated upper bound for the collision likelihood and subsequently identify the smallest possible NMAC analogs. We also demonstrate the NMAC analogs can be used to support modeling and simulation activities.

/pdf/a-quantitatively-derived-nmac-analog-for-smaller-unmanned-322b1jx5or.pdf

A Quantitatively Derived NMAC Analog for Smaller Unmanned Aircraft Systems Based on Unmitigated Collision Risk

/pdf/correlated-encounter-model-for-cooperative-aircraft-in-the-1n9jygblxc.pdf

Correlated Encounter Model for Cooperative Aircraft in the National Airspace System; Version 2.0

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Correlated Encounter Model for Cooperative Aircraft in the National Airspace System; Version 2.0