Uncertainty Characterization for 3D Object Detection Algorithms
Bibliographic record
Abstract
Deep learning has long been the preferred method in acquiring accurate object detection in the context of autonomous driving. Research advancement in deep learning object detection keeps pushing the detection performance - typically measured by mean- average-precision (mAP) - to new records in open datasets like KITTI and NuScene. However, as the mAP measure of performance is based on a fixed threshold value of intersection of union (IOU), it overlooks the object localization error represented by the Euclidean distance between the detected object and ground truth. This paper presents a novel method to evaluate object detection algorithm performance by characterizing localization error and uncertainty. This study selects two different stereo vision algorithms and a corresponding Lidar algorithm for evaluation. The proposed method finds the uncertainty is more significant in the depth direction than in the lateral direction, giving the spatial uncertainty distribution an elliptical shape. The results also show that the error does not always increase monotonically with respect to the depth and is algorithm dependent. The uncertainty characteristics captured from this paper can improve object tracking and multi-sensor fusion performance.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".