Variational Bayesian Based Localization for Intelligent Vehicle Using Lidar and GPS Data Fusion: Algorithm and Experiments
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Bibliographic record
Abstract
Accurate localization is crucial for safe operation of intelligent vehicle (IV). In practice, global positioning system (GPS) signals sometimes may be contaminated or lost, resulting in inaccurate positions of IV. In this article, the distance of IV’s position between previous frame and current frame is derived from Lidar point cloud registration. A novel slide window variational Bayesian (VB) based localization method is proposed for IV with multiple dynamics by fusing GPS and Lidar. In the proposed method, the state of IV, the motion model identity of IV, the measurement loss identity, and the loss probability of measurement are jointly estimated by the VB technique. The effectiveness of the proposed localization method is validated by simulations and field experiments.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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