UNIVERSITY OF CALGARY Performance Evaluation of Sensor Combinations for Mobile Platoon Control
Bibliographic record
Abstract
Autonomous relative navigation of vehicles may soon be feasible. The concept of Collaborative Driving Systems (CDS) involves linking several vehicles together in a platoon. This will have many benefits, including increasing road capacity, improving safety, and reducing driver fatigue and stress. This thesis is focused on examining various positioning sensors for potential use in a CDS. Firstly, the use of GPS as a relative positioning sensor is examined. Tests were conducted using four instrumented vehicles, each equipped with a precise GPS position and heading determination system. This enabled the relative position and velocity estimation between vehicles as well as between antennas with a constant inter-antenna baseline, using carrier phase and Doppler observations. Relative position accuracy was shown to be within a few centimetres, while relative velocity was accurate to a few centimetres per second. Secondly, various sensors were mounted on mobile robots. The lead robot was manually controlled, while another robot was left to autonomously follow. The sensors on the robots include GPS, a digital camera, and a laser scanner. Results show that GPS gives very high accuracy
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".