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arXiv:2604.24403 (cs)
[Submitted on 27 Apr 2026]

Title:An Automatic Ground Collision Avoidance System with Reinforcement Learning

Authors:Seyyid Osman Sevgili, Atahan Cilan, Mahir Demir, Özgün Can Yürütken, Ümit Can Bekar
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Abstract:This article evaluates an artificial intelligence (AI)-based Automatic Ground Collision Avoidance System (AGCAS) designed for advanced jet trainers to enhance operational effectiveness. In the continuously evolving field of aerospace engineering, the integration of AI is crucial for advancing operations with improved timing constraints and efficiency. Our study explores the design process of an AI-driven AGCAS, specifically tailored for advanced jet trainers, focusing on addressing the AGCAS problem within a limited observation space. The system utilizes line-of-sight queries on a terrain server to ensure precise and efficient collision avoidance. This approach aims to significantly improve the safety and operational capabilities of advanced jet trainers.
Subjects: Machine Learning (cs.LG); Robotics (cs.RO)
ACM classes: I.2.6; I.2.8; I.2.9; J.7
Cite as: arXiv:2604.24403 [cs.LG]
  (or arXiv:2604.24403v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2604.24403

Submission history

From: Atahan Cilan [view email]
[v1] Mon, 27 Apr 2026 12:27:08 UTC (1,503 KB)
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