Advancing the AI-Based Realization of ACAS X Towards Real-World Application
In recent years, artificial intelligence (AI) has been applied to a wide range of safety-critical domains, such as automotive, robotics, and aviation. Especially the automotive and robotics domains have seen a rapid increase in the number of AI-based systems that are being deployed in real-world applications. However, real-world applications in the aviation domain are still sparse, given the challenges of AI engineering in combination with strict safety requirements. A first possible application of AI in the aviation domain might be the future collision avoidance system Airborne Collision Avoidance Systems X (ACAS X). The goal of collision avoidance systems is to issue advisories to the pilot to avoid near mid-air collisions (NMACs). The two important variants of ACAS X for this work are ACAS XA, providing vertical advisories and meant as a drop-in replacement for current systems in commercial air flight, and ACAS Xu, providing horizontal advisories for the ever-growing unmanned aircraft systems market. This work brings both variants closer to real-world deployment by implementing a vertical collision avoidance system, based upon ACAS XA, and a horizontal collision avoidance system, based upon ACAS Xu, for the research flight simulator FlightGear. Using advisories given by this implementation, this work furthermore provides an auto-avoid function that can command an airplane in FlightGear to safely avoid NMACs. Finally, this work will show that the ACAS X implementation can avoid collisions in a simulated environment. For this task, an Operational Design Domain will be defined serving as a basis for safety considerations and evaluating the implementation of the ACAS X. In the end, simulation-based testing will be used separately for VCAS and HCAS showing the successful utilization of advisory predictions as autopilot inputs. Summarizing, this work not only presents an open-source implementation of ACAS X<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</inf> and ACAS Xu for FlightGear but also shows how the generated advisories can be used to successfully avoid NMACs.
Authors
- Akshay Anilkumar Girija (ORCID: https://orcid.org/0000-0002-4384-9739)
- Umut Durak (ORCID: https://orcid.org/0000-0002-2928-1710)
- Thomas Stefani (ORCID: https://orcid.org/0000-0001-7352-0590)
- Elena Hoemann
- Frank Köster (ORCID: https://orcid.org/0000-0001-8684-3698)
- Thomas Krüger (ORCID: https://orcid.org/0000-0001-8984-3853)
- Johann Maximilian Christensen (ORCID: https://orcid.org/0000-0001-9871-122X)
- Sven Hallerbach
Institutions
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Publication Details
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1109/ictai62512.2024.00017
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.53