Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle
Bibliographic record
Abstract
In order to improve the safety, stability, and efficiency of lane change operating, this paper proposes a multivehicle-coordinated strategy under the vehicle network environment. The feasibility of collaborative lane change operation is established by establishing a gain function based on the incentive model. By comparing lane change gain with lane keeping gain, whether it is feasible to perform the collaboration under current conditions can be judged. Based on the model predictive control (MPC), a multiobjective optimization control function for cooperative lane change is established to realize the distributed control. A novel two-stage cooperative lane change framework is proposed, which divides the lane change process into the lane change phase and the longitudinal headway adjustment phase. It is significant to solve the difficult numerical problem caused by the dimension of collision-avoidance constraints and the nonlinearity of vehicle kinematics. In the first stage, the subject vehicle completes lane change operation. Both longitudinal and lateral movements of the vehicle are considered to optimize the acceleration and the error of following distance at this stage; in the second stage, the operation of adjusting longitudinal headway between vehicles in the target lane is completed, and at this period, only the longitudinal motion of the vehicle is considered to optimize the vehicle acceleration error. The rolling optimization time domain algorithm is used to solve the optimization control problem step by step. Finally, based on the US NGSIM open-source traffic flow database, the accuracy and feasibility of the proposed strategy are verified.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".