Strategic car‐following gap model considering the effect of cut‐ins from adjacent lanes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drivers are typically faced with two competing challenges when following a preceding vehicle: they need to leave sufficient space in front to ensure safety, while doing so the probability of cut‐ins by other vehicles increases as the car‐following gap (CFG) becomes large. Therefore, a strategic CFG that addresses both challenges becomes critical. This study proposes a method to address the problem through an overall objective function of CFG and velocity considering the safety hazard and the probability of cut‐ins by other vehicles. Based on this, seeking the strategic CFG translates to finding the optimal solution that minimises the overall objective function. With the support of field data, the method along with concrete models are instantiated and application of the method is elaborated. The method presented in this study can be used to enhance traffic safety and improve traffic management in a connected vehicle environment that promises cooperative adaptive cruise control and cooperative crash avoidance systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it