Visible light communication for intelligent transportation systems: A review of the latest technologies
Bibliographic record
Abstract
In seeking to improve traffic congestion and safety on roads and highways, there has been an increased interest in intelligent transportation systems (ITS). The emerging visible light communication (VLC) technology is a new candidate to enable wireless access in ITS. The purpose of this study is to present a comprehensive review of the current studies related to VLC. Since VLC facilitates illumination and data communication simultaneously, it reduces energy consumption significantly. Additionally, VLC is immune to electromagnetic interference, provides high data security, and utilizes unregulated visible light spectrum, showing promise as a potentially cheaper alternative to existing radio frequency (RF) based technology. Moreover, recent advances in semiconductor materials and solid-state technologies have enabled the development of efficient light-emitting diodes (LEDs) and laser diodes (LDs) which are used as transmitters in a VLC system. Although 10 s of Gbits/s data rate has been demonstrated in indoor VLC links, successful implementation of it in outdoor environments requires further research to overcome the challenges presented by environmental factors, unwanted lights, non-line of sight communication, directional radiation pattern, frequent fragmentation, and so on. Besides, in recent years, semiconductor LDs have been garnering more attention since they can transmit more data over longer distances due to their high quantum efficiency and modulation bandwidth compared to LEDs. As a result, urban planners, policy-makers, transportation engineers, and vehicle manufacturers are considering LD-based VLC to facilitate vehicle-to-vehicle and vehicle-to-infrastructure communication. Thus, this paper reviews and compares the most recent developments in VLC technologies, identifies their benefits and potential use in ITS applications, discusses the probable barriers for their implementation in our existing transportation infrastructure, and suggests future research directions and recommendations to overcome these challenges.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".