Preface: Workshop “NGC of AV: Navigation, Guidance and Control of Autonomous Vehicles”
Bibliographic record
Abstract
Autonomous vehicles (AVs) promise to enhance safety, reduce emissions, and improve transportation system efficiency and reliability. The growing demand for AVs is shaping the future of the automotive industry by transforming the in-vehicle experience and paving the way for large-scale implementation of autonomous driving. The positioning, guidance, and control technologies for future Avs are rapidly advancing. They require on onboard intelligence relying on a suite of sensors and systems such as global navigation satellite systems (GNSS) receivers, mmWave wireless technology (5G/6G), vehicle motion sensors and remote sensing systems including cameras, light detection and ranging (LiDAR) and radar. AVs that can sense the environment and navigating without human input require robust advanced positioning, navigation, and guidance for efficient operation in all environments and weather conditions. The availability of the above sensors and systems in future AVs provides an attractive opportunity to advance the robustness and safety of autonomous driving. This workshop will seek original contributions covering advanced topics related to the state of the art and future trends of positioning and mapping as well as control and guidance technologies for autonomous vehicles and future self-driving. The workshop will be part of the ISPRS Geospatial Week 2023 and is hosted the Arab Academy for Science, Technology, and Maritime Transport (AASTMT) in parallel with several related geospatial workshops.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.050 |
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".