A Hybrid Autonomous Intersection Management for Minimizing Delays Using Fuzzy Logic
Bibliographic record
Abstract
This research proposes a fuzzy logic model to control intersection traffic flow to reduce average delay and increase throughput. To achieve this, vehicles and the controller exchange standard Vehicle-to-Infrastructure (V2I) messages to facilitate cooperation and utilization of the intersection. The proposed approach has been validated using the F1tenth_gym_ros simulation platform and on the Eclipse Mosaic platform. The simulation results show that the proposed approach outperforms the static controllers, 25s and 30s by 35.55% and 33.15%, respectively in terms of delay minimization; and 17.44% and 17.82% in terms of throughput, respectively. The proposed approach also outperforms the state-of-the-art controller by 16.18% in terms of delay minimization and 12.16% in terms of throughput. The results of the paired sample t-test also show that the proposed controller outperforms other controllers in both delay and throughput. This shows the potential to improve intelligent transportation systems using V2X technologies and smart intersection management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".