Automatic path control based on integrated steering and external yaw-moment control
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
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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