Deep Learning Enabled Fine-Grained Path Planning for Connected Vehicular Networks
Bibliographic record
Abstract
In this paper, to alleviate the ever-increasing traffic congestion in urban areas by accommodating higher road traffic, we develop traffic prediction framework together with path planning method for connected vehicular networks. First, through the employment of convolutional neural network (CNN) and residual unit (RN), deep learning (DL) based fine-grained traffic prediction algorithm is designed to obtain the spatial-temporal characteristics of vehicular traffic. The regionally fine-grained traffic prediction framework can realize real-time traffic prediction of future changing trends at each road with a high accuracy and reliability. Second, we propose a gridded path planning method by making use of the traffic prediction information. The accuracy of selected path, complexity of path calculation, and adaptive path adjustment are jointly taken into consideration by achieving the refined traffic regulation in different gridded section. Finally, we utilize the actual vehicle data from the city of Beijing and digital map on OpenStreetMap to validate the effectiveness and reliability of the proposed traffic prediction framework and path planning method. Simulation results demonstrate that the proposed approach is capable of relieving urban traffic congestion based on the existing roadway systems, which can provide methodological guidance for data-intensive traffic management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".