Research on Feature Descriptors for Vehicle Detection by LIDAR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
LIDAR is one of remote sensing technologies to measure the distance between the sensor and objects (e.g. pedestrians, vehicles) with pulsed laser light, accurately. Because of its robustness to dynamic lighting conditions and obtainable high spatial resolution, recognition methods using LIDAR have a good capability in object recognition. This is the reason why utilizing LIDAR for autonomous vehicle and/or supporting safety driving systems. In this paper, we focus on a vehicle detection method using LIDAR, and assess the feature descriptors, including two kinds of our proposal descriptors, for point cloud data. Furthermore, we validate appropriate feature descriptors using Real AdaBoost algorithm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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