In-Lane Localization and Ego-Lane Identification Method Based on Highway Lane Endpoints
Bibliographic record
Abstract
The low-cost global navigation satellite systems combined with an inertial navigation system (GNSS/INS) used most frequently for vehicle localization show errors up to 10 m, approximately, even in open-sky environments such as highways. To reduce this error on highways, this paper proposes a localization method based on lane endpoints. Since a lane endpoint frequently appears on a road and can be detected in close proximity even by a low-cost monocular camera, it is a very useful landmark for precise localization. However, the lane width is generally less than 3.5 m, and the localization error from the GNSS is about 10 m. Therefore, if an ego-lane is not identified, the lane endpoints detected in an ego-lane can be falsely corresponded to the lane endpoints in the other lane of a map. This paper proposes an in-lane localization method that uses lane endpoints, the relation between a camera and a road, and the estimated vehicle’s orientation from a map. In addition, this paper proposes an ego-lane identification method that generates a hypothesis about an ego vehicle position per lane by using the proposed in-lane localization method and verifies each hypothesis by the projection of lane endpoints and an additional landmark such as a road sign. The average error of the proposed in-lane localization method is 0.248 m on highways. The success rate of the proposed ego-lane identification method is 99.28% by one trial and reaches 100% by fusing the results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".