A Cooperative Trajectory Optimization Algorithm for Connected Vehicles in Merging Zones
Bibliographic record
Abstract
Traffic flow optimization and trajectory guidance in merging zones have significant implications for improving capacity and reducing time consumption. The development of V2X communication provides new insights to solve this problem by tackling the information and releasing trajectories schemes. Therefore, this paper aims to discuss the trajectory management in the merging zone for ACC vehicles. A vehicle dispatching and car-following model is proposed to generate steady traffic flow first. An algorithm framework for consecutive traffic flow is presented with the idea of FIFO rules. Then, a two-step method for an individual vehicle is discussed in detail to compute a trajectory. The first step is to select and determine the priority of the optional gaps. The next step is to verify the options’ feasibility, decide on the target gap, and output the trajectories to merge successfully. Numerical experiments validate that the proposed method guarantees safe driving and provides relatively smooth trajectories to the vehicles. Furthermore, increased capacity and higher velocity are observed in a comparative experiment. The cooperative optimization algorithm could be applied efficiently in practice and benefit from its rapid response and low computation complexity.
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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 | 0.000 | 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".