Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations

• URL: http://arxiv.org/abs/2401.07353v2
• Date: Sat, 3 Feb 2024 14:55:07 GMT
• Title: Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations
• Authors: Usman Gohar, Michael C. Hunter, Agnieszka Marczak-Czajka, Robyn R. Lutz, Myra B. Cohen, Jane Cleland-Huang
• Abstract summary: Small Unmanned Aircraft Systems (sUAS) have gained widespread adoption across a diverse range of applications. FAA is developing a UAS Traffic Management (UTM) system to control access to airspace based on an sUAS's predicted ability to safely complete its mission. This paper explores stakeholders' perspectives on factors that should be considered in an automated system.
• Score: 40.00051324311249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Small Unmanned Aircraft Systems (sUAS) have gained widespread adoption across a diverse range of applications. This has introduced operational complexities within shared airspaces and an increase in reported incidents, raising safety concerns. In response, the U.S. Federal Aviation Administration (FAA) is developing a UAS Traffic Management (UTM) system to control access to airspace based on an sUAS's predicted ability to safely complete its mission. However, a fully automated system capable of swiftly approving or denying flight requests can be prone to bias and must consider safety, transparency, and fairness to diverse stakeholders. In this paper, we present an initial study that explores stakeholders' perspectives on factors that should be considered in an automated system. Results indicate flight characteristics and environmental conditions were perceived as most important but pilot and drone capabilities should also be considered. Further, several respondents indicated an aversion to any AI-supported automation, highlighting the need for full transparency in automated decision-making. Results provide a societal perspective on the challenges of automating UTM flight authorization decisions and help frame the ongoing design of a solution acceptable to the broader sUAS community.

Related papers

• Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility [0.0]
  Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) Increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments.
  arXiv  Detail & Related papers  (2024-06-28T19:09:55Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management [0.0]
  We develop a contingency management problem as a Markov Decision Process (MDP) and integrate it into the AAM-Gym simulation framework. This enables rapid prototyping of reinforcement learning algorithms and evaluation of existing systems.
  arXiv  Detail & Related papers  (2023-11-17T13:54:02Z)
• Autonomous Advanced Aerial Mobility -- An End-to-end Autonomy Framework for UAVs and Beyond [0.7252027234425332]
  The article proposes a scalable autonomy and autonomy framework consisting of four main blocks: sensing, perception, planning, and controls. The perspective aims to provide a holistic picture of the autonomous advanced aerial mobility field and its future directions.
  arXiv  Detail & Related papers  (2023-11-08T05:54:22Z)
• Toward collision-free trajectory for autonomous and pilot-controlled unmanned aerial vehicles [1.018017727755629]
  This study makes greater use of electronic conspicuity (EC) information made available by PilotAware Ltd in developing an advanced collision management methodology. The merits of the DACM methodology have been demonstrated through extensive simulations and real-world field tests in avoiding mid-air collisions.
  arXiv  Detail & Related papers  (2023-09-18T18:24:31Z)
• Assurance for Autonomy -- JPL's past research, lessons learned, and future directions [56.32768279109502]
  Autonomy is required when a wide variation in circumstances precludes responses being pre-planned. Mission assurance is a key contributor to providing confidence, yet assurance practices honed over decades of spaceflight have relatively little experience with autonomy. Researchers in JPL's software assurance group have been involved in the development of techniques specific to the assurance of autonomy.
  arXiv  Detail & Related papers  (2023-05-16T18:24:12Z)
• Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers [0.0]
  Dense and complex air traffic scenarios require higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCO) use today. This paper proposes using a graph convolutional reinforcement learning method operating in a multiagent setting where each agent (flight) performs a CD&R task, jointly with other agents. We show that this method can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing operational transparency issues.
  arXiv  Detail & Related papers  (2022-06-15T09:06:58Z)
• Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
  A fully-autonomous aerial robot for high-speed object grasping has been proposed. As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface. Our approach has been validated in a challenging international competition and has shown outstanding results.
  arXiv  Detail & Related papers  (2021-12-10T11:49:51Z)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.