V2V Communication-Assisted Transmit-Waveform Selection for Cognitive Vehicular Radars
Why this work is in the frame
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Bibliographic record
Abstract
Vehicular radar is one of the key components for connected and autonomous vehicles (CAVs). Through adaptive transmit-waveform selection, cognitive vehicular radar (CVR) can be developed to support advanced driver assistance systems (ADAS). In this paper, we study the improvement of CVR tracking performance with the assistance of 5G vehicle-to-vehicle (V2V) communications. The model of cognitive dynamic system (CDS) and its function of cognitive risk control (CRC) is incorporated in the design. Specifically, the perceptor of CVR has flexible filtering formulation, which will take the expanded form when V2V messages are available; on the other hand, the executive of CVR has flexible operational modes, which is expanded when unexpected risk needs to be brought under control. Simulation results have shown that the proposed method will improve tracking accuracy significantly in an uncertain and dynamic environment.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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