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 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".