Adaptive cruise control radar‐based positioning in GNSS challenging environment
Bibliographic record
Abstract
Autonomous and land vehicles’ navigation in urban canyons requires aiding from other systems to the Global Navigation Satellite System (GNSS). This kind of environment is characterised by containing high rise buildings and long tunnels which interfere with the GPS satellite's signals causing its partial or total blockage. Therefore, the utilisation of another positioning source with high fidelity solution is essential during long outage periods. The Adaptive Cruise Control (ACC) system is a critical unit in the Advanced Drive Assistant System. The ACC measures the relative speed and distance between the on‐board vehicle and the vehicle in front. In this study, the ACC radar and an azimuth gyroscope are utilised to produce a self‐contained positioning system. The position solution of this system is utilised to update the Inertial Navigation System during the GNSS outage periods. The proposed system was tested over real road trajectories which were conducted in an urban canyon to validate the efficiency of the system.
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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".