Group Frenet Frame CAV Path Planning on Highways
Bibliographic record
Abstract
Connected autonomous vehicle (CAV) systems could bring considerable benefits to our daily lives, and possibly outperform single autonomous vehicle (AV). Nevertheless, the real-time determination of the optimal route for each connected autonomous vehicle (CAV) within a continuous space presents a considerable challenge. This difficulty arises from the exponential growth of potential motion combinations for CAVs, considering the diverse road geometries they encounter. This article proposed a CAV group planning framework to overcome this challenge. The framework works hierarchically. The global and local controllers play a crucial role in generating long-term reference paths for each CAV by employing a versatile road geometry model capable of accommodating diverse road shapes. Initially, waypoints are extracted utilizing this generalized road geometric model. Subsequently, potential combinations of waypoints are generated by considering the CAV group as a fleet. Finally, optimal waypoint combinations are assigned to each CAV by considering the CAVs’ own benefit and road usage. Reference paths for each CAV are generated using the selected waypoints and are passed on to the CAVs and roadside units (RSUs) layer. The CAVs and RSUs generate short-term motion, given the reference paths. This is operated in the Frenet frame, and the optimal motion for each CAV is selected in the aspect of the entire CAV fleet. The proposed framework is tested in simulation and has shown the ability to generate safe and sound paths under various road geometries with obstacles and in mixed traffics in real time.
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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 | 0.001 |
| 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".