Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance
- URL: http://arxiv.org/abs/2605.20595v1
- Date: Wed, 20 May 2026 01:04:17 GMT
- Title: Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance
- Abstract summary: This paper presents the first controller-coupled characterization of an all-airborne, sidelink-class, intent-first vehicle-to-vehicle (V2V) tactical neighborhood exchange stack.<n>We implement and evaluate this model on an all-airborne V2V stack using sidelink-class C-V2X modules with authenticated freshness checks.
- Score: 1.0152838128195467
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dense low-altitude aerial operations require more than pre-flight route coordination and last-resort collision avoidance. Once aircraft are airborne, disturbances can emerge on timescales shorter than strategic reauthorization can absorb, while collision avoidance is too late and disruptive to serve as routine traffic management. Although tactical separation is recognized as the intermediate layer, realizing it at scale requires a deployable neighborhood communication mechanism that provides fresh, trusted information for local coordination. This paper presents what is, to our knowledge, the first controller-coupled characterization of an all-airborne, sidelink-class, intent-first vehicle-to-vehicle (V2V) tactical neighborhood exchange stack for dense Unmanned Aircraft System Traffic Management (UTM) operations. Unlike awareness-only broadcast, the proposed exchange combines refreshed state and intent beacons for local awareness, cooperative perception, and degraded-mode assessment with event-triggered messages for yielding, sequencing, release, and contingency coordination. We implement and evaluate this model on an all-airborne V2V stack using sidelink-class C-V2X modules with authenticated freshness checks. Evaluation uses a scenario-driven, high-volume stress campaign supported by real-time, field-anchored infrastructure. Results show that V2V reduces stale-belief divergence, preserves observability through cooperative perception, rejects invalid tactical messages, suppresses false local inference, and structures shared-resource coordination. The implemented stack provides a viable communication layer for tactical separation in lower-to-moderate regimes, but transitions toward guarded fallback as density, impairment, and complexity increase. These findings position intent-first aerial V2V as a bounded enabler for scaling tactical coordination in disturbance-driven urban airspace.
Related papers
- VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation [50.18051979020699]
VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
arXiv Detail & Related papers (2026-09-02T11:32:52Z) - Semi-Decentralized Multi-Spacecraft Collision Avoidance under Communication Constraints [22.71668728031368]
Current spacecraft collision-avoidance operations rely on intermittent ground-station contacts, requiring operators to plan with delayed and asynchronously updated information.<n>We formulate a semi-decentralized POMDP (SDec-POMDP) where we govern information propagation directly by realistic ground-station visibility windows.<n>Results show that semi-decentralized planning recovers near-centralized maneuver quality while requiring 28.5% fewer synchronization events than continuous coordination.
arXiv Detail & Related papers (2026-07-29T07:48:18Z) - Distributed Coordination for Resilient Multi-UAV Remote Sensing: A Photovoltaic Inspection Case Study [0.0]
deploying multiple UAVs for remote sensing enables proportional reductions in mission time.<n>In inspection campaigns, where mission value depends on complete coverage and the usability of every capture, a centralized ground-station coordinator is a single point of failure.<n>We propose the textbfSLink, an inter-agent communication infrastructure that non-invasively extends any existing aerial framework with peer-to-peer coordination capability.
arXiv Detail & Related papers (2026-07-27T14:17:48Z) - End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation [59.188006170485046]
E2E-CDiff is an end-to-end conditional diffusion framework for controllable and realistic scenario generation.<n>Differentiable guidance regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors.<n>E2E-CDiff also performs competitively as a learning-based ego planner.
arXiv Detail & Related papers (2026-07-21T02:13:25Z) - LAEI: Layered Autonomous Edge Intelligence Framework for Robust UAV Swarm Operations [3.8233569758620054]
Layered Edge Intelligence (LAEI) is a UAV-swarm framework that combines onboard learned policies with lightweight mission-level supervision.<n>LAEI reduces mission completion time and improves operational efficiency while maintaining collision-aware distributed UAV-level decision-making.
arXiv Detail & Related papers (2026-06-08T06:50:15Z) - Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions [6.590573149504977]
Large-scale Unmanned Aerial Vehicle (UAV) failures can split an unmanned aerial vehicle swarm network into disconnected sub-networks.<n>We present Physics-informed Adrial Graph Learning algorithm (PhyGAIL) that adopts centralized training with decentralized execution.
arXiv Detail & Related papers (2026-04-17T07:06:42Z) - Communications-Aware NMPC for Multi-Rotor Aerial Relay Networks Under Jamming Interference [10.131651799774108]
"tilt-to-translate" maneuvers can inadvertently align antenna directions with communication partners, causing severe degradation under interference.<n>This paper presents a communications-aware control framework that combines a high-level max-min trajectory generator with an actuator-level Model Predictive Controller.<n>Results show a near two-order-of-magnitude increase in minimum end-to-end capacity, markedly reducing outage events, with moderate average-capacity gains.
arXiv Detail & Related papers (2026-03-30T14:05:54Z) - Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective [55.919842734983156]
CoSA is a transferable attack framework that operates within a shared low-dimensional semantic space.<n>CoSA consistently outperforms state-of-the-art transferable attacks.
arXiv Detail & Related papers (2026-01-30T15:48:11Z) - A Future Capabilities Agent for Tactical Air Traffic Control [0.0]
Agent Mallard is a rules-based tactical agent for control in systemised airspace.<n>The architecture is intended to combine model-based safety assessment, interpretable decision logic, and tractable computational performance.
arXiv Detail & Related papers (2026-01-07T14:19:46Z) - Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection [0.6999740786886536]
Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations.<n>Traditional misbehaviour detection approaches, which rely on plausibility checks and statistical methods, suffer from high False Positive (FP) rates.<n>We present Attention In Motion (AIMformer), a transformer-based framework specifically tailored for real-time misbehaviour detection in vehicular platoons.
arXiv Detail & Related papers (2025-12-17T14:45:33Z) - SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks [73.41290017870097]
SecDiff is a plug-and-play, diffusion-aided decoding framework.<n>It significantly enhances the security and robustness of deep J SCC under adversarial wireless environments.
arXiv Detail & Related papers (2025-11-03T11:24:06Z) - UNCAP: Uncertainty-Guided Planning Using Natural Language Communication for Cooperative Autonomous Vehicles [79.10221881250759]
Uncertainty-Guided Natural Language Cooperative Autonomous Planning (UNCAP) is a vision-language model-based planning approach.<n>It enables CAVs to communicate via lightweight natural language messages while explicitly accounting for perception uncertainty in decision-making.<n> Experiments across diverse driving scenarios show a 63% reduction in communication bandwidth with a 31% increase in driving safety score, a 61% reduction in decision uncertainty, and a four-fold increase in collision distance margin.
arXiv Detail & Related papers (2025-10-14T21:09:09Z) - Convergence of Communications, Control, and Machine Learning for Secure
and Autonomous Vehicle Navigation [78.60496411542549]
Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations.
This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
arXiv Detail & Related papers (2023-07-05T21:38:36Z) - Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning
Framework for Congestion Control in Tactical Environments [53.08686495706487]
This paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network.
We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link.
arXiv Detail & Related papers (2023-06-27T16:15:15Z) - Interruption-Aware Cooperative Perception for V2X Communication-Aided
Autonomous Driving [49.42873226593071]
We propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP) for V2X communication-aided autonomous driving.
We use historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue.
Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.
arXiv Detail & Related papers (2023-04-24T04:59:13Z)
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.