Kinematic Analysis of the Planar Motion of Vehicles when Traveling Along Tractrix Curves
Bibliographic record
Abstract
Abstract The kinematic analysis of the planar motion of vehicles, such as common cars, buses, and trucks, when traveling along linear and circular tractrices at low speeds, is proposed here based on the fundamentals of the kinematics of planar mechanisms. In particular, the analysis of the vehicle chassis motion, with chassis represented as a drawbar connecting the back and front-wheel centers, is developed and formulated by determining the moving and the fixed centrodes. The proposed formulation was implemented in matlab to simulate and analyze the vehicle motion at low speeds, as the front-wheel center follows a straight line or a circle and, correspondingly, the back-wheel center traces a linear tractrix or one of the inner and outer circular tractrices, according to the exit from or entrance of the vehicle into a roundabout. Significant numerical and graphical results allow the validation of the proposed formulation, which represents useful tool to predict the vehicle behavior at low speeds during parking, changing of lanes, and entering and leaving roundabouts, thereby increasing the safety for bicycles, motorcycles, and pedestrians, along with the design of safe roads and highways.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".