Adaptive Information Synchronization and Prediction for Digital Twin-Enabled Autonomous Driving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While adopting Digital Twin (DT) systems keeps a growing trend in autonomous vehicle (AV) applications, ensuring efficient and accurate information synchronization between the physical entity layer and DT layer remains a major challenge. Solely using a fixed synchronization policy often suffers from high communication overhead, increased latency and low resource utilization efficiency in variable environments. Additionally, relying on a universal prediction model is hard to maintain prediction accuracy across varying traffic scenarios. In this paper, we propose an adaptive information synchronization and prediction model switching method. Our method can adaptively adjust the DT-physical layer synchronization frequency based on the similarity estimation between the two layers, achieved by switching between short-term and long-term prediction methods dynamically to better adapt to time-varying traffic characteristics, thereby improving the accuracy of the prediction results. Concurrently, the DT-enabled system utilizes the predicted vehicle state information to assist AVs in making overtaking decisions, optimizing traffic flow and improving road safety. Experimental evaluations show that our scheme effectively balances accuracy and resource efficiency in synchronization of DT, demonstrating its suitability for variable and resource-constrained vehicle network environments.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it