Implementation and Evaluation of a Traffic Light Assistance System Based on V2I Communication in a Simulation Framework
Bibliographic record
Abstract
Cooperative Intelligent Transportation Systems (C-ITS) make the exchange of information possible through cooperative systems that broadcast traffic data to enhance road safety. Traffic light assistance (TLA) systems in particular utilize real-time traffic light timing data by accessing the information directly from the traffic management center. To test the reliability of a TLA system based on networked intervehicular interaction with infrastructure, we present in this paper an approach to perform theoretical studies in a lab-controlled scenario. The proposed system retrieves the traffic light timing program within a range in order to calculate the optimal speed while approaching an intersection and shows a recommended velocity based on the vehicle’s current acceleration and speed, phase state of the traffic light, and remaining phase duration. Results show an increase in driving efficiency in the form of improvement of traffic flow, reduced gas emissions, and waiting time at traffic lights after the drivers adjusted their velocity to the speed calculated by the system.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".