Gradient-Based Auto-Exposure Control Applied to a Self-Driving Car
Why this work is in the frame
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Bibliographic record
Abstract
As vision plays a central role in the operation of autonomous cars, one key challenge is that the limited dynamic range of camera sensors can only capture a certain portion of the scene radiance. This can lead to loss of information from images, which affects the performance of autonomous cars. To address this, we present an implementation of an exposure compensation method from the literature to auto-adjust camera exposure for the cameras mounted on a self-driving car. Furthermore, we extend this algorithm to incorporate gain compensation. The algorithm dynamically changes camera exposure time and gain settings with the intent to maximize image gradient information. The algorithm was evaluated in both indoor and outdoor environments, and experimental results demonstrate the effectiveness of our implementation. An open-source implementation of our technique is provided.
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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 | 0.000 | 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