Value Matters: A Novel Value of Information-Based Resource Scheduling Method for CAVs
Bibliographic record
Abstract
The Internet of Vehicles (IoV) can support applications in connected autonomous vehicles (CAVs), the implementation of which can effectively improve traffic efficiency. However, safety-related CAV applications have very strict requirements on the reliability and latency of each packet, which is difficult to achieve due to limited resources and the high dynamics of CAVs. In this paper, we investigate communication resource scheduling for remote autonomous driving (AD) to improve the performance of the remote control system when network resources are constrained. Specifically, we introduce a novel performance metric, i.e.,value of information(VoI), to capture how sending a packet will affect the performance of CAV driving safety and efficiency, i.e., the value of the packet on the considered CAV system. The formulation of VoI is derived using the Lyapunov optimization method, and the lower-bound for the performance of the AD system with a VoI-based scheduling strategy is analyzed. Then, a communication resource scheduling approach is proposed based on the VoI of each packet. Simulation results demonstrate that the proposed VoI-based resource scheduling approach is capable of accurately assessing the impact of information transfer on system performance, while ensuring the CAV's safety and enhancing traffic efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".