A Collective and Continuous Rule Learning Framework Towards Enhanced Driving Safety and Decision Transparency
Bibliographic record
Abstract
Deep reinforcement learning (DRL)-based driving policies enable connected and autonomous vehicles (CAVs) to adapt to complex environments but lack guarantees of safety and decision transparency. For this reason, we propose a hybrid decision-making system that integrates DRL with a learnable, rule-based safety module, wherein the latter offers enhanced driving safety and decision transparency. However, as collision data is limited for a single CAV during the DRL learning process, it becomes necessary for CAVs to collectively learn such a rulebased safety module to accelerate the learning process. Given the potential for conflicting safety rules learned from different CAVs, we propose a two-stage collective rule learning framework for the safety module. In the first stage, the multi-access edge computing (MEC) network aggregates rules from CAVs based on occurrence frequency and generalization capability. In the second stage, CAVs compute rule weights based on local data satisfaction and violations, followed by the MEC network aggregating these weights. The aggregated weights resolve conflicting rules and help filter out insignificant rules to maintain a compact safety module. The safety module makes comprehensive decisions by aggregating all the weighted rules. Simulation results in the highway driving simulator demonstrate that the two-stage collective rule learning framework effectively aggregates learning outcomes from CAVs, and the proposed hybrid decision-making system further reduces collisions compared to the existing approach.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".