Autonomous vehicle relocation problem in a parking facility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autonomous vehicles (AVs) can be stacked behind each other like valet parking due to their self-parking capability to increase land utilization and decrease the parking space allocation. However, such parking arrangements cause blockage and necessitate AVs relocation inside the car-park whenever a blocked AV is summoned. In this paper, we investigate AV parking spot selection based on their arrival and departure times to minimize the number of such relocation movements. We study both full and partial information scenarios in which we have exactly a priori information, and no a priori information about arrival and departure time, respectively. The results show that parking in the spot with the lowest blockage probability can decrease the number of relocation movements. Also, our analysis shows that retrieving vehicles from the rear side does not have a significant impact on the number of relocation movements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it