Impact of Non-platooning Vehicles in Connected Autonomous Vehicle Platooning
Bibliographic record
Abstract
Connected and Autonomous Vehicles (CAVs) are vehicles that detect and communicate with the surrounding vehicles and infrastructure automatically to perform functions such as traffic sign detection, object tracking including vehicles and pedestrians, etc. CAVs offer accurate distance sensing for shorter headway as well as provide reduced reaction time, and ultimately increase the roadway capacity and efficiency. These promising benefits of CAVs can realize through their platooning. CAV platooning is becoming appealing due to the benefits on improved traffic efficiency, reduced fuel consumption and emissions. However, these benefits may not be totally realized in the mixed traffic scenario. For instance, cut-in or cut-through maneuvers by the non-platooning vehicles may be the major obstacle to maintaining platoon integrity. In this paper, not only the implementation of CAV platooning is demonstrated using CARLA but also the impact of the non-platooning vehicles in the CAV platooning in different scenarios is investigated. For this purpose, the non-platooning vehicles have been categorized into priority and non-priority vehicles. Results show how the platooning vehicles are affected by the cut-through vehicles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".