A Novel Traffic Characteristics Aware and Context Prediction Protocol for Intelligent Connected Vehicles
Bibliographic record
Abstract
Digital maps have been installed and attached to vehicles recently. They help with the GPS receivers to determine the relative locations of vehicles to other existing traffic and objects over the road network such as entrance/exit points, obstacles, road intersections, etc. This helps drivers or autonomous vehicles to decide the most appropriate reaction, in terms of speed, take-over, or stop operations ahead of time. Several daily traveling vehicles do not have digital maps. Besides, digital maps are vulnerable to being destroyed or inaccurate. They require regular updates due to the continuous construction and re-design of the road networks. These constructions aimed to enhance the road design and the traffic efficiency there. Moreover, accidents, broken vehicles, traffic congestion, or other ad-hoc obstacles appear unpredictably over the road network. In this article, we aim to introduce a prediction protocol that gathers and analyzes the traffic characteristics of vehicles over the investigated road scenario using wireless transceivers in vehicles. Then, it predicts the physical and traffic context based on the analyzed traffic data. This protocol can replace the absent or broken digital maps in vehicles. It also can be used to verify the correctness of the digital map in vehicles. From the experimental results, we can infer that the proposed protocol has succeeded in predicting the road context over highways and downtown scenarios. More accurate and better predictions are acquired by increasing the percentage of wireless transceiver-equipped 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".