Assessing Distribution Shift in Probabilistic Object Detection Under Adverse Weather
Bibliographic record
Abstract
Object detection is a safety-critical aspect of autonomous driving, allowing vehicles to identify moving objects in the scene for tracking, prediction and decision making. Current detectors, however, tend to provide point estimates for detected objects, which lack information on the variability of the prediction and how well it fits the model that produced the prediction. Proper uncertainty estimation can be incorporated into traditional object detection pipelines to produce a measure of uncertainty alongside traditional point estimate object predictions. In this work, uncertainty estimates are implemented for LiDAR and camera object detectors using Bayesian theory, and the resulting output distributions are assessed using signal detection theory to generate an uncertainty based classifier that can evaluate its own performance. The classifier can be used to track the ratio of false positive to true positive detections, defined as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anomalous detections ratio</i> . Findings from this work indicate that this novel metric is responsive to degraded driving conditions including night time driving and lens obstructions for the RGB camera, while in LiDAR data, the metric is responsive to snowfall and simulated rain conditions. These results are focused on the classification and regression of vehicle objects, making use of the sizeable ground-truth sets for vehicles that are provided in publicly-available autonomous driving data sets.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".