Perspectives of Convertors and Communication Aspects in Automated Vehicles, Part 2: Printed Antennas and Sensors for Automotive Radars
Bibliographic record
Abstract
Automated vehicles are becoming popular across the communities of e-transportation across the globe. Hybrid electric vehicles and autonomous vehicles have been subjected to critical research for decades. The research outcomes pertinent to this topic in the literature have been motivated by the industry and researchers to emphasize automated vehicles. Part 1 of this survey addressed the critical aspects that concern the bidirectional converter topologies and condition monitoring activities. In the present part, 24- and 77-GHz low-profile printed antennas are studied for automotive radar applications. These antennas are mounted on automated vehicles to avoid collision and are used for radio tracking applications. The present paper states the types of antenna structures, feed mechanisms, dielectric material requirements, design techniques, performance parameters, and challenges at 24- and 77-GHz resonating frequency applications. The recent developments in feed methodologies, beam scanning concepts, and the effect of sidelobe levels are addressed. Furthermore, the reasons behind the transition from 24 to 77 GHz are reported in detail. The recent advances in the application of various sensor schemes in an automated vehicle have also been discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".