Current and future trends in wireless enabling technologies for fully automomous cruise cars and their enhancement of road safety
Bibliographic record
Abstract
Recent years have witnessed acceleration in wireless technology breakthroughs that proved to be key technology enablers of a plethora of applications that have shaped our modern society. For road safety and accident preventions, wireless technologies play a pivotal role in saving lives by assisting drivers in detecting potential collisions from blind spots and in inclement weather conditions such as thick fog or heavy rain. These wireless technologies include automotive radars for collision detections, adaptive cruise control system for autonomous cars, the Internet of Things (IoT) and 5G. While the first is considered a mature technology, the others are rich research areas that promise even greater level of driver assistance, and thus an exponential decline in road accidents and a smoother traffic flow and control, with commercialization expected in the 2020-2025 timeframe. This paper presents a detailed study of the aforementioned technologies in terms of current commercial automotive solutions, relevant future research frameworks, research and commercialization timeline, overlap with other wireless technologies such as cellular communications in the context of future 5G and IoT, and underlying physics and electronics. In addition, limitations and design challenges will also be discussed. Finally, an important comparison between the number of road accidents with and without the above technologies is presented. This comparison presents a compelling evidence that wireless technologies for the automotive industry are key to the reduction of fatal road accidents and the savings of millions of human lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".