Sight Distance of Automated Vehicles Considering Highway Vertical Alignments and Its Implications for Speed Limits
Why this work is in the frame
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Bibliographic record
Abstract
Most existing road infrastructures were constructed before the emergence of automated vehicles (AVs) without considering their operational needs. Whether and how AVs could safely adapt to as-built highway geometry are questions that remain inconclusive, and a plausible concern is a challenge from vertical alignments. To fill this gap, this study uses a virtual simulation to investigate the available sight distance (ASD) of AVs on vertical alignments subject to the current highway geometric design specification and its implications for speed limits. According to the scenario generation framework, several scenarios featuring vertical geometric elements and lidar sensors were created and tested. Moreover, the maximum speed for adequate ASD is calculated to determine the AV speed limit, considering safe sight distance and speed consistency requirements. The results indicate that crest curves are not disadvantaged in ASD compared with either sag curves or tangent grades. Only equipped with multichannel lidar and advanced perception algorithms enabling a lower detection threshold would a level 4 AV be compatible with the as-built vertical alignment with a design speed ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> ) of 100 km/h. However, a level 3 AV can only adapt to the vertical profile with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> = 60 km/h. The findings of this study should be of interest to the road-oriented operational design domain and support road administrators in regulating AV safe speeds.
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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 it