Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2607.20547v1
• Date: Mon, 13 Jul 2026 20:11:44 GMT
• Title: Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning
• Abstract summary: This study develops a Deep Q-Network-based Multi-Agent Reinforcement Learning framework for decentralized conflict resolution.<n>The framework supports the simulation-based evaluation of safer AAM conflict-resolution strategies under degraded surveillance conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporarily unavailable. This study develops a Deep Q-Network-based Multi-Agent Reinforcement Learning framework for decentralized conflict resolution among heterogeneous small unmanned aerial vehicles and electric vertical takeoff and landing aircraft operating within a structured three-dimensional corridor. Separate policies are trained for the two aircraft categories using local observations and a 14-action space that includes maintaining course, turning, vertical maneuvering, landing, and speed control. The simulation incorporates aircraft-specific dynamics, energy use, corridor constraints, observation noise, communication delay, information dropout, wind disturbance, actuator uncertainty, and model uncertainty. The trained policies are evaluated across 90 combinations of traffic density and minimum separation thresholds. Loss-of-separation frequency and duration generally increase with traffic density and separation requirements, although most events are resolved within 1s. Under safe conditions, agents maintain their motion approximately 79% of the time. During conflicts, turning accounts for 33% of actions, followed by maintaining motion at 29%, speed control at 25%, and vertical maneuvers at 13%. Six Pareto-optimal configurations reveal trade-offs between safety and corridor capacity. The framework supports the simulation-based evaluation of safer AAM conflict-resolution strategies under degraded surveillance conditions.

Related papers

• Control-Informed Constraint Adaptation in Minimum-Time Trajectory Planning for Autonomous Racing [41.40585589054522]
  Trajectory planners assume perfect tracking and remain blind to execution errors.<n>We introduce a control-informed online trajectory planning framework that learns from its own execution errors.<n>We show that our approach reduces lap time by 1.8,s without increasing computational burden.
  arXiv  Detail & Related papers  (2026-08-14T16:32:12Z)
• Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing [1.7396274240172123]
  This paper addresses defensive blocking in autonomous racing, where a vehicle must prevent a faster opponent from overtaking while operating near its dynamic limits.<n>We formulate defense as a spatial occupancy regulation problem via a hierarchical reinforcement-learning guided model predictive control framework.<n>The framework achieves a 33.3 ms mean solve time (13.9 ms) supporting real-time high-speed adversarial interaction.
  arXiv  Detail & Related papers  (2026-07-23T06:50:59Z)
• Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination [2.399137242179316]
  We propose a multi-agent coordination framework that uses minimum time-to-reach (TTR) as a unifying metric for priority assignment, temporal separation, and safety filtering.<n>We focus on the problem of coordinating multiple aerial vehicles merging into an air corridor while maintaining safe separation between vehicles.
  arXiv  Detail & Related papers  (2026-05-20T02:16:49Z)
• RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework [49.531001563853984]
  RAD-2 is a unified generator-discriminator framework for closed-loop planning.<n>It produces diverse trajectory candidates, while an RL-optimized discriminator reranks these candidates according to their long-term driving quality.<n>It reduces the collision rate by 56% compared with strong diffusion-based planners.
  arXiv  Detail & Related papers  (2026-04-16T17:59:44Z)
• eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces [3.719121868494767]
  This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential algorithm affect eVTOL energy consumption.<n>We analyze 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft.<n>MVP-based deconfliction is energy-efficient, but tail cases reach 44% overhead at the highest densities due to sustained multi-aircraft conflicts.
  arXiv  Detail & Related papers  (2026-04-07T17:07:39Z)
• Transformer-based Multi-agent Reinforcement Learning for Separation Assurance in Structured and Unstructured Airspaces [3.719121868494767]
  We show that a single encoder configuration can yield near-zero near mid-air collision rates and shorter loss-of-separation infringements than the deeper configurations.<n>Our results suggest that the newly formulated state representation, novel design of neural network architecture, and proposed training strategy provide an adaptable and scalable decentralized solution for aircraft separation assurance.
  arXiv  Detail & Related papers  (2026-01-07T21:18:28Z)
• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking [43.11267507022928]
  This paper proposes a novel mitigation framework on 3D LiDAR scene against adversarial noise.<n>We employ the least-squares graph tool to reduce the induced positional error of each detection's centroid.<n>An extensive evaluation study on the real-world V2V4Real dataset demonstrates that the proposed method significantly outperforms both single and multi-agent tracking frameworks.
  arXiv  Detail & Related papers  (2025-07-07T08:41:08Z)
• Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games [42.74003740156243]
  We address the problem of agile 1v1 quadrotor pursuit-evasion, where a pursuer and an evader learn to outmaneuver each other.<n>We propose an Asynchronous Multi-Stage Population-Based (AMSPB) algorithm to tackle these issues.<n>Within this framework, we train neural network controllers that output either velocity commands or body rates with collective thrust.
  arXiv  Detail & Related papers  (2025-06-03T13:19:23Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks [0.0]
  We present a reinforcement learning framework to provide autonomous self-separation capabilities within AAM corridors. The problem is formulated as a Markov Decision Process and solved by developing a novel extension to the sample-efficient, off-policy soft actor-critic (SAC) algorithm. A comprehensive numerical study shows that the proposed framework can ensure safe and efficient separation of aircraft in high density, dynamic environments.
  arXiv  Detail & Related papers  (2023-08-09T13:44:35Z)
• iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning [57.24340061741223]
  We introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios. Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
  arXiv  Detail & Related papers  (2023-06-09T20:12:02Z)

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.