AeroCast: Probabilistic 3D Trajectory Prediction for Non-Cooperative Aerial Obstacles via Transformer-MDN Architecture
arXiv:2606.25122v1 Announce Type: cross Abstract: Autonomous aerial vehicles operating in shared airspace must predict the future positions of non-cooperative obstacles to plan evasive maneuvers before a collision becomes unavoidable. Unlike cooperative systems that share intent, non-cooperative obstacles such as birds, uncontrolled drones, or debris exhibit multi-modal motion that deterministic predictors cannot adequately represent. Existing methods either rely on recurrent encoders that propagate temporal information sequentially, limiting their ability to capture long-range kinematic precu
The proliferation of uncooperative aerial objects and the increasing sophistication of autonomous aerial vehicles necessitate advanced prediction capabilities for safe operations.
This research directly addresses a major safety and operational bottleneck for autonomous aerial vehicles, enabling their wider deployment in complex environments.
Autonomous aerial vehicles become significantly more capable of operating safely in shared, uncontrolled airspace by predicting highly unpredictable obstacle movements.
- · Autonomous Aerial Vehicle Manufacturers
- · Defense Industry
- · Logistics/Delivery Companies
- · AI/ML Developers
- · Aircraft Accident Investigators (potentially fewer incidents)
- · Companies reliant on less accurate prediction methods
Reduced collision risk for autonomous aerial vehicles through enhanced trajectory prediction.
Accelerated adoption and operational scope of autonomous drones in diverse civil and military applications.
Potential for new regulations and standards based on probabilistic prediction capabilities, reshaping airspace management paradigms.
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Read at arXiv cs.LG