Bibliographic record
Abstract
The majority of research on autonomous vehicle technology these days focuses on car technology, with little emphasis on road infrastructure, such as geometric design. The goal of this research project is to bridge this gap and make the geometric road design for autonomous vehicles sustainable, especially focusing on sight distance requirements which play a critical role in roadway safety. The stopping sight distance model is frequently used in road geometrics because it offers enough time to avoid accidents and is efficient. The stopping sight design model is implemented in this study effort for autonomous vehicle technology. To start, the autonomous vehicle technology is investigated, and a substantial difference between autonomous vehicle technology and human-driven vehicle technology is determined in order to use the stopping sight distance model. A literature review is also conducted for the geometric design of the road for both human-driven and self-driving vehicles. For the autonomous vehicle, the AASHTO model developed for human-driven vehicles is applied and adapted, resulting in the optimal geometric design for the autonomous vehicle. Additionally, a simulation model is designed in Matlab to test different configurations for the placement of LiDAR sensor on autonomous vehicles in specific scenarios that are critical to roadway safety such as when there is an overpass over a vertical sag curve.
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 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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".