LOW-COST CLOUD-BASED HD-MAP UPDATES FOR INFRASTRUCTURE MANAGEMENT AND MAINTENANCE
Bibliographic record
Abstract
Abstract. Recently, HD maps have various merits for achieving the highest level of self-localization accuracy, keeping track of the state of the road infrastructure and maintenance, and providing an indication if any repairs are required. Therefore, it is essential to keep the HD map up to date. However, the process of updating the HD map is exorbitant because the HD map is created using expensive sensor setups, and updating the map frequently via these setups will be costly. In this paper, a full pipeline is proposed for updating the HD map via a crowdsourced dataset that is collected with low-cost smartphone sensors. Furthermore, an Android application is developed and installed on a smartphone to collect the raw data. Once the dataset is collected from the area of interest, it will be uploaded automatically to the cloud server that is connected to the HD map database. Then, object detection, depth estimation, and matching algorithms are triggered on the cloud server to keep updating the HD map database. The positions of the detected objects from the crowdsourced dataset are estimated by using fused outputs of deep learning models and the Global navigation satellite system (GNSS) of a smartphone and then compared with the objects in the HD map through matching algorithms. The proposed model is considered the first comprehensive pipeline approach for updating HD maps with high a cost-effective and efficient solution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".