Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management - Route Alteration

Aug 3, 2026

Upload by Patrick

A novel approach to detour management in Urban Air Traffic Management (UATM) is presented through knowledge representation and reasoning. The work targets the complexity of UAM detours by enabling rapid identification of safe and efficient routes within a carefully sampled environment. Implemented in Answer Set Programming, it uses non-monotonic reasoning and a two-phase dialogue between a human manager and the UATM system, accounting for safety and potential impacts. Robustness and effectiveness are validated using queries from two simulation scenarios, supporting symbiosis of human expertise and advanced AI.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Urban Air Mobility challenges

Problem setting and contributions

Related Works

Layered system approaches

Risk and collision assessment

Deep Learning for ATM

arXiv :2308 .06411v1 [ cs .AI] 11 Aug 2023 Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration Jeongseok Kim [jeongseok.kim@sk.com](jeongseok.kim@sk.com) AIX SK Telecom Seoul, 04539, Republic of Korea Kangjin Kim [kangjinkim@cdu.ac.kr](kangjinkim@cdu.ac.kr) Department of Drone Systems Chodang University Jeollanam-do, 58530, Republic of Korea Editor: Kangjin Kim Abstract This paper introduces a novel approach to detour management in Urban Air Traffic Management (UATM) using knowledge representation and reasoning. It aims to understand the complexities and requirements of UAM detours, enabling a method that quickly identifies safe and efficient routes in a carefully sampled environment. This method implemented in Answer Set Programming uses non-monotonic reasoning and a two-phase conversation between a human manager and the UATM system, considering factors like safety and potential impacts. The robustness and efficacy of the proposed method were validated through several queries from two simulation scenarios, contributing to the symbiosis of human knowledge and advanced AI techniques. The paper provides an introduction, citing relevant studies, problem formulation, solution, discussions, and concluding comments. Keywords: UAM, UATM, KRR, Answer Set Programming, Articulating Agent 1. Introduction Urban Air Mobility (UAM) has become a hot topic in the aviation industry due to technology and novel mobility options. However, the aviation industry’s infrastructure is unprepared for this paradigm shift. The Korean Urban Air Mobility Concept of Operations [1] by MOLIT describes it asa paradigm with unprecedented challenges, such as integrating low-altitude flights into dense urban environments, high-density air traffic management, and a transition to fully autonomous operations by 2035 . This mobility transition necessitates the participation of numerous stakeholders from diverse industries, resulting in a complex landscape devoid of defined data sharing mechanisms. 1 Citation: Jeongseok Kim, et al. Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration Advances in Artificial Intelligence and Machine Learning. 2023;8(6):120 . © 2 . [https://www.oajaiml.com/](https://www.oajaiml.com/)—August 2023 Jeongseok Kim, et al. The absence of standardization impedes UAM operations. Never before has it been more crucial to have an air traffic management system that can adapt to a heterogeneous environment and scale to accommodate growing data volume and complexity. This investigation employs UAM Air Traffic Management (UATM) solutions to address these obstacles. We create a graph model of the UAM airway network. Each node in this concept is a”vertiport” —a vertical airport—and each connection represents a route between two adjacent vertiports. A human traffic manager supervises landing and departure operations at each vertiport and notifies the UATM system of any traffic issues. The research presented in this paper offers an in-depth scenario illustrating the communication process of route change instructions to the relevant agents, consequently causing a modification in their currently charted routes. It is crucial to understand that the operational scope of each UATM system is not determined by its physical proximity to a vertiport, but is autonomously determined by the UATM itself. Given the constraints of communication range, this level of autonomy becomes increasingly significant. In some circumstances, a UATM might need to transmit instructions to certain agents via the UATM Network [2], as highlighted by our research. This investigation aims to unravel the operational complexities inherent to the rapidly evolving UAM field. By doing so, our hope is to contribute to the development of a more robust, flexible, and scalable future urban air traffic management system. This system would be capable of accommodatin

FAQs

What problem does the paper address in UAM operations?

It addresses how to manage route detours in Urban Air Traffic Management when standardization is lacking and environments involve complex, heterogeneous stakeholders and data flows.

How does the proposed method handle detour management?

It models the UAM airway network as a graph and uses non-monotonic reasoning in Answer Set Programming with a two-phase dialogue between a human supervisor and the UATM system.

How was the method evaluated?

The robustness and efficacy were validated through several queries from two simulation scenarios, checking safe and efficient route identification and impacts.

Aug 3, 2026

Upload by Patrick