Rewardless Reinforcement Learning Method for Cooperative Sensing Optimization in Connected and Autonomous Vehicles
Bibliographic record
Abstract
Enhancing safety and efficiency in connected and autonomous vehicles (CAVs) is a paramount concern, necessitating advanced sensing, communication and computing solutions. Despite the potential of utilizing the sensors deployed on roadside infrastructure to improve driving reliability, conflicts between heterogeneous needs of CAVs and real-time available resources present significant challenges. To address these, this paper introduces an innovative intelligence-guided reinforcement learning (IRL) framework designed to improve the the reliability and effectiveness of sensing information processing and transmission. Firstly, we propose a comprehensive road sensor networks (RSNs) framework that considers the heterogeneous sensing requirements of CAVs and available communication and computing resources, ensuring the reliability of sensing assistance provided to CAVs across varied and complex driving environments. Furthermore, we formulate the sensing information processing and transmission issue as an active inference to construct higher-level cognition about the present environment without relying on rewards. To address this, an IRL-guided method is implemented. “Intelligence” as a metric is leveraged for guiding the learning process and improving adaptability in diverse driving environments. Through extensive simulations, our proposal demonstrates superior performance over existing approaches, showing marked improvements in detection accuracy, latency reduction, and efficient data handling across various traffic scenarios.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".