Modified Gipps model: a collision-free car following model
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
Car following (CF) models are used in microscopic traffic simulation tools to help assess the effects of a new road design or to assess the effect of change in traffic flow. In 1981, Gipps developed a collision avoidance CF model using the Newtonian laws of motion to describe the motion of each vehicle in a stream of traffic. It is one of the most widely used CF models in both research and practice. Although it is claimed that the Gipps model produces collision-free results, the model produces a collision when the intention of the following vehicle is to brake harder than the perceived deceleration of lead vehicle. For the ease of simulations, a traffic simulation tool is expected to not show unrealistic crashes. This study was carried out to make the Gipps model collision-free in all conditions. It first highlights the conditions where the original Gipps model produces a collision. Then the study derives an equation for a collision-free Gipps CF model. This modified Gipps CF model produces collision-free results that always maintain a safe spacing with the lead vehicle.
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