METIS: Crowdsourced Lane Line Map Construction
Bibliographic record
Abstract
High-Definition (HD) maps are critical for autonomous vehicles, but their creation and maintenance via traditional survey fleets is expensive and time intensive. We introduce METIS (Map Element Telemetry Information System), a novel system that constructs HD maps by leveraging a previously underutilized data source - semantic primitives (e.g., vectorized lane lines), already generated by the perception systems of consumer-grade vehicles. METIS employs an end-to-end pipeline that integrates feature matching, RANSAC-based outlier rejection, and factor graph optimization to align noisy crowd-sourced observations and correct significant GNSS and perception errors. We evaluated METIS using over 100 miles of data from diverse road environments, achieving a relative geometric accuracy with a 2-sigma error of 0.7 meters against ground-truth data in our freeway test. Crucially, we validated our system's practical utility by deploying a METIS-generated map in a Level-2 autonomous vehicle and successfully completing a multimile hands-free autonomous test drive on a public freeway. Our findings demonstrate that crowd-sourcing semantic primitives are a viable, cost-effective, and scalable pathway for creating and maintaining the high-fidelity maps required for autonomous driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".