Vehicle state prediction for outdoor autonomous high-speed off-road UGVs
Bibliographic record
Abstract
This paper describes a method of vehicle state prediction for an autonomous high-speed off-road Unmanned Ground Vehicle (UGV). Effective vehicle state prediction will allow a UGV to plan its navigation such that states (such as vertical acceleration induced by the terrain roughness) never exceed a desired threshold. In this paper a model of an n-wheeled generic vehicle is used determine its dynamics. Using a known terrain input profile the vehicle's output states are predicted using the developed n-wheel model. Simulated results of this vehicle state prediction approach are presented, as well as experimental tests using a UGV platform called Loc8. The experimental results used a 3D point cloud to determine the terrain input profile. Methods from the literature are tested against the developed n-wheeled vehicle state prediction method. Results show this n-wheel technique presents both advantages and disadvantages in comparison with existing techniques. The proposed approach predicts the average absolute acceleration much closer to the measured average absolute acceleration than existing approaches.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".