A Survey on Compressed Sensing in Vehicular Infotainment Systems
Bibliographic record
Abstract
Vehicular infotainment systems have attracted great interests from both academia and industry. A vehicular infotainment system includes two parts: 1) the information part and 2) the entertainment part, which are integrated together to provide a unified platform to drivers and passengers. Although some excellent works have been done on vehicular infotainment systems, there are still some significant challenges that need to be addressed. Recently, compressed sensing (CS) has emerged as a new technique, which could be used to tackle the challenges in vehicular infotainment systems. In this paper, we present a comprehensive survey and research challenges on the applications of CS in vehicular infotainment systems, including object tracking for driving safety, privacy protection and security, vehicular crowdsensing, vehicular communications, road traffic estimation, video streaming, and object recognition. In addition, we compare the traditional methods and CS methods in solving the main problems in vehicular infotainment systems. Moreover, research challenges of applying CS in vehicular infotainment systems are 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".