Dynamic Modelling of Docking Autonomous PODs in Tandem Configuration
Bibliographic record
Abstract
An adaptable transportation concept is proposed; comprising a fleet of autonomous PODs that can merge and separate based on passengers’ demand. The purpose is to match the number of seats with the number of passengers, thereby reducing vehicle size and energy consumption. It enables passengers’ in-person communication and simultaneous arrival. Since each POD has its own motor, if full power is not needed, one of the motors can be turned-off to save energy. The merging process is investigated so as to find the safe docking speeds when two PODs merge in tandem configuration. If the docking is not done at the right speed, it may cause damage to the vehicle, or else be inefficiently slow. The PODs are represented by finite element models, which are simulated to determine the safe merging speeds. The speeds are determined for different docking scenarios and POD materials; ranging from 1.4-16 km/h. The safe speeds depend on the type of material and adopted damage criterion; Nonmetallic materials showed higher tolerance than metallic materials in response to docking impact. As a recommendation for future work, other materials and configurations can be investigated, and the effect of the proposed system on traffic conditions can be evaluated.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".