Socially Game-Theoretic Lane-Change for Autonomous Heavy Vehicle Based on Asymmetric Driving Aggressiveness
Bibliographic record
Abstract
The existing autonomous driving systems present poor performance for heavy vehicles, and face challenges gaining acceptance from surrounding vehicles in the interactive scenarios due to the deficiency of sociality. Therefore, a socially game-theoretic decision-making method is proposed in this study. Firstly, the concept of asymmetric driving aggressiveness is introduced to construct the decision-making model, ensuring driving safety and sociality across different vehicle types. Secondly, a socialized lane-change model for autonomous heavy vehicles is proposed based on the asymmetric aggressiveness and game theory with incomplete information. Subsequently, the cooperativeness of the interactive vehicle is estimated to eliminate the uncertainty through the combination of trajectory prediction and driving environment assessment. Finally, two conventional decisionmaking models are used as benchmarks to validate the effectiveness and robustness of the proposed method using naturalistic driving data. The results demonstrate that the socially game-theoretic lane-change model can generate safe and socialized decisions by estimating the cooperativeness of interactive vehicle. Besides, the proposed method exhibits enhanced social-friendliness with minimal impact on other vehicles and excellent robustness in navigating dynamic and complex traffic environment. Despite a slight reduction in driving efficiency, it still demonstrates a better adaptability and compatibility for autonomous heavy vehicles because the depressed performances are not the primary concerns.
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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) | 0.001 | 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".