Automatic path control based on integrated steering and external yaw-moment control
Bibliographic record
Abstract
Nowadays improving safety is an indispensable part of research issues in the automotive industry. Due to increased travelling time, accident potentials and also traffic congestions, automated vehicles are seen as a way to increase freeway capacity and vehicle speed while reducing accidents resulted from human errors. In order to guide a vehicle automatically, vehicle lateral motion should be controlled, active steering control (ASC) and direct yaw-moment control (DYC) are two common methods to control the vehicle lateral dynamic, automatically. For higher vehicle lateral acceleration, where the tyres will not be capable of producing enough lateral forces (yaw-moment), ASC could not be useful. In such situation, the advantages of DYC can be clearly observed. Indent In this paper, a novel optimal control law is proposed to control the vehicle path, automatically. The control law uses the vehicle dynamic variables such as the yaw and lateral velocities, lateral offset, and the heading error as well as the road-related variables. These are the road curvature and the lateral offset between the desired path and the vehicle as the feedback/feed-forward signals to produce both the front steering angle and the external yaw-moment signals as the control efforts. Simulation results illustrate the dominant power of the front steering/DYC in the control of the vehicle lateral motion.
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 | 0.000 |
| 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) | 0.001 | 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".