Path Optimization and Decision Making Mechanism for Driverless Vehicles in Intelligent Transportation Systems under Vehicle-Circuit Collaboration
Bibliographic record
Abstract
In this study, we construct an unmanned vehicle path optimization model based on fast extended random tree, and after kinematic modeling of unmanned vehicles, we introduce the artificial potential field method to improve the fast extended random tree algorithm, and apply it to the path optimization of unmanned vehicles.According to the swarm intelligence perception decision-making algorithm, the end-to-end unmanned vehicle decision-making model based on vehicle-circuit collaboration is constructed.The effectiveness of this paper's driverless path optimization and decision-making model based on vehicle-circuit collaboration is examined.The waiting time for red light of this paper's model is shorter than other path planning schemes, and the vehicle passing benefit at intersections is the highest.The passing benefit values of this paper's model are 70.3% and 46.8% higher than Maxband scheme and Synchro scheme, respectively.In the right-turn simulation experiments, the main vehicle speed change shows a tendency to accelerate and the path is basically overlapped with the edge of the lane without offsetting the center of the lane.In the normal driving speeds of [14,38], the fuel consumption of the driverless vehicle shows an up and down trend, and the carbon dioxide emission varies with the fuel consumption.The total cost of traveling decreases with increasing speed.
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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".