Integrated Air-Ground Vehicles for UAV Emergency Landing Based on Graph Convolution Network
Bibliographic record
Abstract
With unmanned aerial vehicle (UAV) technologies advanced rapidly, many applications have emerged in cities. However, those applications do not widely spread as the safety consideration hinders the UAV from integrating into the civilian environment. This work focuses on investigating the UAV emergency landing problem which is a critical safety functionality of UAV. This work proposed a graph convolution network (GCN)-based decision network to learn by imitating the human pilots’ landing strategy. To alleviate the needs of a large amount of real-world data for model training, the proposed model allows to be trained in a simulated environment and then transferred to the real-world scenario due to the separation of domain-specific terrain classes and domain-independent topological structures among down-looking camera images. The GCN-based decision network can be coupled with a topological heuristic to improve the performance of action prediction in an emergency situation. To evaluate the proposed method, this work implemented a simulation environment for collecting data and testing the UAV emergency landing. The empirical results in both simulated and real-world scenarios show that the proposed methods can outperform the state-of-the-art counterparts in terms of predictive accuracy and success landing rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".