Digital Map Based Navigation System For Autonomous Vehicle with DGPS Localization
Bibliographic record
Abstract
Autonomous Vehicles (AV) can navigate itself from point `A' to point `B' without\nthe aid of humans. Research on autonomous vehicles were primarily focused on the\nlocalization, navigation and path planning schemes. This led to numerous methods\nin each of the elds of focus. This research focuses on creating a scheme for the\nautonomous vehicle to navigate using minimal sensors and get maximum data/infor-\nmation from the map. At rst a digital map contains various structures and each has\nan associated database. This database contains the details of the environment. At\npresent these data are manipulated for use by humans and for this map to be used\nwith autonomous vehicle require more sensors. This work designs maps for use with\nautonomous vehicle and navigates using di erential GPS (dGPS) of high accuracy\nfor localization. Then the vehicle gets path and directions from digital map and nav-\nigates using multiple waypoints that are provided by the path. Finally, the scheme is\ntested and demonstrated through simulation and test results.
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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".