Modeling vehicle collision instincts over road midblock using deep learning
Bibliographic record
Abstract
The present research aims to understand the safety over the midblock road sections and proposes a safety framework using the conventional Time to Collision (TTC) measure. In the present work, the safety framework underlines a supporting structure connecting the actions of the surrounding vehicles and assesses the collisions changes for a given subject vehicle. The Framework principally checks the likelihood of lateral overlap and the time gap between the subject vehicle and its surrounding vehicles. Later, for the trajectory data development, an automated trajectory data development tool is built with the help of image processing for generating the trajectory data from the study sections. In supporting the developed safety framework, the lateral movement of the vehicles is modeled precisely with the help of deep learning. Further, the conceptualized safety framework is tested with the developed trajectory data sets over the study sections. From the results, it is observed that, in mixed traffic, the collision points are over the entire geometry of the study section. In the case of homogeneous traffic, the collision instincts are clustered toward the median lanes. With the advancement of technology, trajectory data development can be a real-time exercise, and the safety framework can be implemented. By applying the study methodology, the critical spots over the road network can be flagged for better treatment and improve safety over the sections.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".