Impacts of Cooperative Adaptive Cruise Control Links on Driving Comfort under Vehicle-to-Vehicle Communication
Bibliographic record
Abstract
Although automated vehicles could release drivers from the driving task, there are still passengers sitting in the vehicle. It is required that the driving comfort of passengers should be guaranteed. Cooperative adaptive cruise control (CACC) vehicle is of the one important type of automated vehicles using vehicle-to-vehicle (V2V) communications with various communication links. Different V2V communication links might have different driving comfort. Then, this paper focuses on exploring which link type for CACC vehicles is better from the perspective of improving driving comfort. To deal with this, car-following models of manual-driven vehicles (MDV) and CACC vehicles were first described. Then, simulations were performed using these car-following models, in which various CACC feedback link types, CACC penetration rates, and flow scenarios were taken into consideration. Simulations outputted microcosmic trajectory data of vehicles, based on which the driving comfort was evaluated using the comfort index described by the International Organization for Standardization (ISO) 2631-1. From the driving comfort perspective, simulation results suggest that CACC should monitor the immediately preceding vehicle and the third vehicle ahead when CACC penetration rates are less than approximately 50%. Additionally, if CACC penetration rates exceed 50%, the better choice is that CACC receives feedback links from two immediately successive vehicles ahead.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".