Cooperative Lane‐Changing Control for CAVs at Freeway On‐Ramps considering Vehicle Dynamics
Bibliographic record
Abstract
This study proposes a cooperative lane‐changing control framework for multiple vehicles in freeway ramp merging areas, aiming to achieve safe and efficient merging. Specifically, multiple connected and automated vehicles (CAVs) form triplets to participate in cooperative lane‐changing. The framework consists of two stages: Longitudinal Headway Adjustment (LHA) and Lane‐Changing Execution (LCE). In the LHA stage, a centralized longitudinal controller is developed based on the vehicle’s longitudinal dynamics model to optimize the longitudinal velocity of the cooperative vehicles and create suitable gaps for merging vehicles. In the LCE stage, an optimal lane‐changing reference trajectory is generated using a quintic polynomial and a lateral controller is designed based on the vehicle’s lateral dynamics model. Model Predictive Control (MPC) is utilized for trajectory tracking. The simulation results obtained using MATLAB/Simulink, GPOPS, and CarSim demonstrate that the proposed control strategy can be applied to different vehicle speed control scenarios. Taking a specific velocity combination as an example, the cumulative control errors in the longitudinal and lateral directions for PV (Preceding Vehicle), SV (Subject Vehicle), and FV (Following Vehicle) are 1.4014 m, 0.5631 m, and −0.7601 m, respectively, satisfying the safety distance requirements. Compared to the Linear Quadratic Regulator (LQR) control, the proposed strategy improves control efficiency by 145.03%, 69.64%, 43.18%, and 67.61% in terms of comprehensive spacing errors, synthesized acceleration, front wheel angle, and speed fluctuation, respectively. These research findings highlight the superior performance of the proposed control strategy in terms of traffic efficiency, comfort, safety, and vehicle stability.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".