High-performance Trajectory Optimization for Automated Parking via Half-space Constraining Theory
Bibliographic record
Abstract
摘要： 轨迹规划是车载自动泊车系统中的重要功能，而现有的泊车轨迹规划算法无法兼顾算法泛化性、计算精度、求解时效性以及结果最优性。现采用基于数值优化的轨迹规划技术路线，首先将泊车轨迹规划任务表述为一则通用的最优控制问题；随后提出半空间约束理论，结合概略轨迹先验信息将原本具有高维度、强非凸非线性特点的名义避障约束简化为线性不等式约束，继而利用信任域约束进一步降低线性不等式约束的规模；最后调用非线性规划求解器对简化后的最优控制问题进行数值求解，可在极短时间内生成高精度数值最优泊车轨迹：将上述泊车轨迹规划方法命名为预设空间快速优化法。大量仿真试验表明，在同样使用混合A*搜索算法提供先验的概略轨迹的前提下，预设空间快速优化法的求解成功率、计算耗时以及结果最优性均优于OBCA(Optimization-based collision avoidance)、LIOM(Lightweight iteratwe optimization method)等主流泊车轨迹优化算法。
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".