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--- |
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license: apache-2.0 |
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tags: |
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- flight-planning |
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- transformer |
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- coordinate-prediction |
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- sequence-to-sequence |
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- count-classification |
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--- |
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# Flight Plan Coordinate Prediction Model (Seq2SeqCoordsTransformer) |
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The encoder-decoder transformer model was trained for an AI flight planning project. Predicts normalized coordinates directly and waypoint count via classification. |
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## Model Description |
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Seq2SeqCoordsTransformer architecture using `torch.nn.Transformer`. Predicts normalized lat/lon coordinates autoregressively and waypoint count (0-10) via classification head on encoder output. |
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* Embed Dim: 256, Heads: 8, Enc Layers: 4, Dec Layers: 4, Max Waypoints: 10 |
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## Intended Use |
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Research prototype. **Not for real-world navigation.** |
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## Limitations |
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Accuracy depends on data/tuning. Fixed max waypoints (10). Not certified. **Architecture differs significantly from previous versions in this repo.** |
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## How to Use |
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This requires loading the custom `Seq2SeqCoordsTransformer` class and weights. Generation requires autoregressive decoding and taking the argmax of the count logits. |
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Read this article - https://medium.com/ai-simplified-in-plain-english/building-a-transformer-model-with-seq2seq-architecture-for-flight-planning-0bdd1fecaefe |
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## Training Data |
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Trained on `frankmorales2020/flight_plan_waypoints` - https://huggingface.co/datasets/frankmorales2020/flight_plan_waypoints. |
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## Contact |
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Frank Morales, BEng, MEng, SMIEEE (Boeing ATF) - https://www.linkedin.com/in/frank-morales1964/ |
