Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter reviews state-of-the-art sensors, instrumentation and algorithms used for localization of autonomous vehicles. The current localization approaches for autonomous driving involve localizing by satellite navigation systems, vehicle motion sensors, range sensors, and vision sensors. The chapter presents current localization approaches, which are categorized as global localization, relative localization, and simultaneous localization and mapping (SLAM). In relative localization, visual odometry (VO) is specifically highlighted with details. The chapter describes the two main approaches of VO: appearance-based and feature-based approaches. Three main approaches of SLAM, namely, Kalman filter, particle filter, and graph-based approaches, are presented. The chapter presents estimation, filtering, and sensor fusion techniques for cooperative localization. It finally reviews some current localization techniques in use and discusses potential solutions to these gaps, as well as future directions for localization in 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.
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) | 0.002 | 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 it