Improvement of Image-based Lane Marker Detection Algorithm using Sensor Fusion
Bibliographic record
Abstract
Assistive driving systems like Lane Keeping Assist predominantly utilize image processing for lane marker detection to localize the vehicle, however, image data faces challenges, such as sensitivity to weather and road conditions and a low update rate due to computational demands. To address these issues, this study proposes a novel approach for local positioning by fusing image data with dashboard speed, IMU (accelerometer and gyroscope sensors) measurements, and GPS data. An Extended Kalman Filter as a stochastic estimator is used to estimate road-vehicle states and enhance lane marker detection accuracy under various conditions. The algorithm’s performance was tested across diverse driving scenarios, including different speeds, road curvatures, and poor visibility conditions simulating Canada’s winter weather. Results indicate that the proposed sensor fusion algorithm significantly reduces RMS and maximum error in estimating lane lateral offset, relative heading angle, and velocity, especially where image-based methods falter due to noise or temporary loss of functionality.
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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 | 0.000 |
| 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 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".