Research on dynamic constraint mechanism and motion control of intelligent vehicles based on preview distance optimization under complex road conditions
Bibliographic record
Abstract
Aiming at the coupling and conflict issues between intelligent vehicle dynamics characteristics and path tracking system under complex road conditions, this paper investigates the dynamics constraint mechanism and motion control of intelligent vehicles, and proposes an intelligent vehicle lateral–vertical cooperative control method based on optimized preview distance. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicle. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the “human-vehicle-road” system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, hardware-in-the-loop platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 m, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".