Path Search Algorithm for Parking Spaces Based on Artificial Intelligence Algorithm
Bibliographic record
Abstract
In order to alleviate the problems of short supply of parking spaces and traffic congestion, intelligent driving solutions have emerged.Automatic parking has now become the first application scenario for driverless driving due to the more fixed scenario and lower traveling speed.In this study, the traditional A* algorithm is improved using the cost function, and the hybrid algorithm of parking space path search and planning is designed by combining the improved A* algorithm with the Reeds-Shepp curve, and then combined with the collision constraints to improve the algorithm's path planning performance.The results of simulation experiments and in-loop test experiments show that the maximum lateral error and heading error are low in parallel and perpendicular parking scenarios, and it is found that the average lateral error during the whole parking process is only 0.177m in the inloop test, which is a good tracking effect for vehicles.The path search and planning algorithm designed in this paper can better realize the autonomous parking function and has high tracking accuracy and stability in the simulation scenario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".