Implications of as‐built highway horizontal curves on vehicle dynamics/kinematics characteristics under adaptive cruise control
Bibliographic record
Abstract
Abstract Due to road curvature and sensors’ limited field of view, as‐built highway curves would pose an operational challenge to the adaptive cruise control (ACC) system and its shared control. However, very few studies explored the adaptability of ACC system‐dedicated vehicles (V‐ACC) considering the vehicle‐road geometry interaction. Therefore, the objectives of this study are twofold: (i) investigating the implications of horizontal curves on V‐ACC dynamics and kinematics characteristics; and (ii) evaluating V‐ACC's adaptability from the safety, comfort, and speed consistency (S‐C‐S) aspects. To this end, a PreScan/CarSim/MATLAB/Simulink co‐simulation platform is established and it is validated by OpenACC database followed by designing many tests featuring circular curve radius ( R C ), desired speed ( V de ), and clearance. The impact mechanism of geometric features was analysed by interpreting dynamics and kinematics characteristics along curves and critical features were further extracted by reference to S‐C‐S thresholds. The results show that: (i) either smaller R C or higher V de causes those characteristics toward their S‐C‐S margins; (ii) neither sideslip nor rollover occurs, and speed consistency is good in most R C conditions; and (iii) drivers can follow the leading car comfortably with V de = 40, 80–100 km/h but feel uncomfortable when V de = 50–70 km/h and R C approaches its lower bounds.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".