Multitask Vehicle Signal Recognition With Dual‐Speed Adaptive Weighting
Bibliographic record
Abstract
In mixed traffic environments, the accurate identification of vehicular devices’ modulation schemes, communication protocols, and emitter device information directly affects perception capabilities toward surrounding vehicles and infrastructure. However, existing studies predominantly focus on single‐dimensional information analysis, resulting in limited completeness and accuracy in signal feature interpretation. This paper proposes a multitask learning framework (DSR‐CNN‐LSTM) for collaborative identification of this information. Furthermore, to mitigate task conflicts and noise interference, a dual‐rate adaptive weight adjustment strategy is developed to optimize model performance through dynamic balancing of task learning rates and gradient update speeds. Experimental results demonstrate the superior performance of the DSR‐CNN‐LSTM framework in complex communication environments: Modulation recognition accuracy shows improvements of 20.67%, 10.38%, and 9.96% on three open‐source datasets, while the weighted average recognition accuracy for communication protocols and emitter device information achieves enhancements of 45.52%, 72.21%, and 11.11%, respectively. The proposed model outperforms existing methods in both recognition precision and anti‐interference capabilities, providing novel technical insights and solutions for the advancement of intelligent connected vehicle technologies.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".