Evaluation of the Effects of Autonomous Vehicles on Highway Geometric Design
Bibliographic record
Abstract
Automation Vehicle is emerging as an innovative technology that will eventually replace human-driven vehicles in the immediate future. Since individual road construction for AVs may be impractical in most areas, they will rely on the same infrastructure as human driver vehicles. The purpose of this project is to determine the optimal sensors configuration, including Lidar height, detection range, field of view, and resolution for the safe operation of AV on mixed traffic (autonomous and human-driven vehicles) on existing highways without any geometric design modifications. Since the physical testing on public roads is unsafe, costly, and not consistently reproducible, a simulation-based framework based on the Automated Driving Toolbox in MATLAB was presented. The first aspect is the impact of AV on geometric design, stopping sight distance, and highway alignment. The second section discusses the Lidar system, which serves as the AVs "eye," as well as current Lidar types, technical parameters, and Lidar function. Further, a comparison of commonly used AV sensors, including Lidar, camera, and radar, was provided. Then a MATLAB virtual simulation framework was proposed. This project provides important information regarding sensors configuration that helps AV safely adapt to existing highway infrastructures without modifying the geometric design. Keywords:
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 | 0.001 | 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 | 0.001 | 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".