Optimizing Non-Glare Zone Width of Adaptive Driving Beam (ADB) Using Fuzzy Logic Control
Bibliographic record
Abstract
Adaptive driving beam (ADB) is an advanced vehicle forward-lighting system that automatically adapts its beam patterns to create a non-glare zone around vehicles, providing good long-range visibility for the driver without causing an uncomfortable glare for other road users. The performance of the ADB system is affected by the non-glare zone width. A narrow non-glare zone could create indirect glare in the side rearview mirrors of preceding vehicles during sharp turns while widening it results in poor road illumination. This research studies the trade-off relationship between glare and road illumination when altering the width of the non-glare zone in different driving scenarios. The study is conducted by using virtual driving simulation tools to simulate an ADB vehicle on four S-curve roads with minimum curvatures varying from 25 m to 100 m. Lux data are collected and processed using a fuzzy logic controller to mimic a human test driver to find the best non-glare zone width for balancing the trade-off. The research developed a design methodology allowing for a better understanding of the effect adjusting the width of the ADB non-glare zone has on ADB performance and improved ADB non-glare zone width optimum control system design.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".