复杂交通场景下道路毫米波雷达-相机融合自动标定
Bibliographic record
Abstract
针对传统标定方法依赖先验信息导致人工成本高、难以适应动态场景的问题，提出一种基于改进轨迹关联与动态采样的路侧毫米波雷达-相机自动标定方法（ICAT-BANSAC-LM）。首先，构建改进的轨迹感知关联策略（ICAT），通过引入角度距离估计实现无反射标记物的自动数据关联；其次，引入基于动态贝叶斯网络的自适应采样一致性（BANSAC）算法，在前端进行动态加权采样与异常值过滤；最后，采用Huber损失函数改进的Levenberg-Marquardt（LM）算法优化标定结果。实验结果表明，在交通密集、视觉条件差及雨天等复杂场景下，所提算法的重投影误差均方根分别为3.06、4.07、3.67 cm，较随机采样一致性（RANSAC）-LM算法的精度分别提升36.9%、37.6%、37.3%。所提算法显著提升了恶劣环境下的标定精度与鲁棒性，为智能交通系统提供了可靠的技术支持。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".