Digital Twin-Driven Asymmetric Driving Aggressiveness Assessment for Connected Autonomous Vehicles
Bibliographic record
Abstract
Accurately evaluating the driving aggressiveness could greatly improve the driving efficiency for connected autonomous vehicles (CAV) in the mixed traffic. Current aggressiveness evaluation methods merely focus on the collision probability, inadequately assessing hazard levels due to symmetry between interactive vehicles. Meanwhile, existing methods operate on a microcosmic scale, guiding motion planning within short time. With the advent of digital twin technology, aggressiveness assessments of road segments and mesoscopic planning become feasible. Thus, a novel driving aggressiveness assessment model is proposed in this paper based on asymmetric interactions between different types of vehicles. The interaction behaviors are analyzed, and the mathematic model of the aggressiveness is deduced. Subsequently, an application case of the proposed model in lane-change decision-making is, verifying its capacity to generate asymmetric driving behavior. The aggressiveness model provides a new perspective on asymmetric road safety evaluation and heterogeneous driving behavior in connected traffic environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".