A Cooperative Lane-Changing Strategy for Weaving Sections of Urban Expressway under the Connected Autonomous Vehicle Environment
Bibliographic record
Abstract
To alleviate the lane-changing conflicts between weaving vehicles and enhance the traffic efficiency in the weaving section of urban expressway under the connected autonomous vehicle (CAV) environment, a cooperative lane-changing strategy for CAVs is proposed. The strategy consists of an upper layer of decision making, which determines the lane-changing sequences of weaving vehicles based on their lane-changing advantages quantified by a set of utility functions, and a lower layer of control, which generates detailed instructions of speed adjustments and lane-changing manoeuvres for weaving vehicles. To verify the effectiveness of the proposed strategy under different traffic demand settings, a numerical simulation, including a base case and a control case, is conducted. Then, to further verify the effectiveness of the proposed strategy for the mixed traffic state and compare its performance with the existing CAV lane-changing method, benchmark and comparison tests with six different market penetration rates (MPRs) of CAVs are carried out under the congested demand setting. In addition, the delay improvement ratio, inverse time-to-collision, and ratio of large deceleration time are selected as performance indicators to investigate the effect of the proposed strategy on enhancing the operational efficiency, traffic safety, and passenger’s comfort within the weaving section. According to the simulation results, the overall efficiency, safety and comfort in the weaving section under the CAV environment, are all improved, when the proposed strategy is applied to weaving vehicles. The proposed strategy is also superior to the existing CAV lane-changing method on maintaining traffic efficiency and safety. Therefore, the proposed cooperative lane-changing strategy, based on CAV technologies, shows good potential in solving the problem of lane-changing conflicts within the weaving section and facilitating the traffic management and traffic control of urban expressway.
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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".