Terrain roughness assessment for human assisted UGV navigation within heterogeneous terrains
Bibliographic record
Abstract
This paper presents a new, computationally inexpensive, terrain roughness assessment technique. The technique, targeted for high speed unmanned ground vehicles (UGVs), considers no previous knowledge about the terrain but a priori knowledge about the vehicle's characteristics such as damping ratio, natural frequency and wheel configuration. The approach aims to allow fast, smooth and safe transition between different terrains (e.g. pavement, grass, sand, etc.) with no additional computation effort to detect the terrain transitions and cope with the associated complexities. This technique although developed for UGVs is applicable to current commercial vehicles as a driving assistant system which will increase the safety of motor vehicles (not only UGVs). The terrain profile is considered as a frequency band limited random variable. Accordingly, terrain roughness is described in terms of a set of stochastic parameters. This paper derives the required resolution of the terrain perception system as function of the vehicle's characteristics needed by the UGV to make effective maneuvers. A simulation of a vehicle changing its speed according to the perceived terrain roughness illustrates the implementation of the proposed technique. The results also show how the vehicle is able to autonomously traverse at high speed unknown terrains containing different roughness regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".