Anticipatory adaptive cruise control (AACC) for enhanced performance in mixed traffic flow
Bibliographic record
Abstract
Adaptive cruise control (ACC) systems improve traffic efficiency by optimizing acceleration and deceleration, maintaining steady speeds, and reducing unnecessary braking, leading to smoother flow, lower fuel consumption, and reduced emissions. This paper introduces anticipatory adaptive cruise control (AACC), which enhances ACC through vehicle-to-vehicle communication, adjusting speeds based on multiple vehicles ahead. The study examines the effects of AACC in mixed traffic environments using MIXEM, a MATLAB microsimulation, modelling car following behavior of both manually-driven and AACC-equipped vehicles. Simulations cover various scenarios, including signalized intersections and fixed bottlenecks. Results show AACC halved clearance times at intersections and transformed traffic from wide moving jams to synchronized flow, altering the fundamental diagram. AACC effectively reverses capacity drops, increases discharge flow rates, and shifts the point of critical density, demonstrating its potential as a traffic regulator by promoting optimal driving behavior among nearby manually-driven vehicles.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".