Lane-Changing Model of Intelligent Connected Vehicle Considering the Factor of Turn Signal
Bibliographic record
Abstract
Unsafe lane-changing behaviors can easily lead to traffic accidents. Drivers usually turn on their turn signals to signal surrounding vehicles before changing lanes. At present, there is a lack of consideration of the impact of turn signals on the lane-changing behavior of intelligent vehicles. Therefore, based on the cellular automata theory, this paper improves the lane-changing rules in the STNS model and proposes a vehicle safe lane-changing model. The model considers the priority scheduling problem of different vehicles’ driving behavior when changing lanes, the influence of driver’s subjective factors on the driving speed when changing lanes, and the relationship between vehicle speed and safe lane-changing distance. After discussion and analysis, the model can reduce the number of lane changes of vehicles, increase the average speed of vehicles, and increase the traffic flow. It provides theoretical support for the safe lane-changing behavior of intelligent networked vehicles in the new era.
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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".