Balancing Computation Speed and Quality: A Decentralized Motion Planning Method for Cooperative Lane Changes of Connected and Automated Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on the multi-vehicle motion planning (MVMP) problem for cooperative lane changes of connected and automated vehicles (CAVs). The predominant decentralized MVMP methods can hardly explore and utilize the cooperation capability of a multi-vehicle team, thus they usually lead to low-quality solutions. This paper proposes a two-stage MVMP framework to find high-quality online solutions. Concretely, at stage 1, the CAV platoon transfers from its original formation to a sufficiently sparse formation; at stage 2, all the CAVs simultaneously change lanes with collision avoidance implicitly ensured. The CAVs only involve longitudinal rather than lateral motions at stage 1, thus the collision-avoidance constraints can be easily handled. Since stage 2 begins with a sparse formation, the implicitly ensured collision avoidance can be completely omitted then. Through this, the proposed method avoids directly handling the challenging collision avoidance conditions, thereby being able to compute fast. As the vehicles run cooperatively and simultaneously at either stage, the obtained solutions are near-optimal. The completeness, effectiveness, and quality of the proposed two-stage MVMP method are validated through theoretical analysis and comparative simulations.
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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 | 0.000 | 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 it