Intelligent Impaired Driving Detection and Road Emergency Services System
Bibliographic record
Abstract
On average every day in Canada 3.5 people die, and 175 get injured due to drunken driving. In 2015, the highest impaired driving rates were reported in the Northwest Territories, Saskatchewan and Yukon. In Saskatchewan, there were 575 incidents per 100,000 of the population, which is almost twice as high as Alberta's. Even with the preventive measures like advertising and campaigning about drunk driving and its consequences, it is not much help in reducing these accidents. Problem with the existing system is that the number of patrol officers is very less in comparison to the vehicles on the road. So it is challenging to detect an impaired driver on the road. It is required to build an intelligent car system that detects impaired driving and warns the driver. The proposed system can be installed in a moving vehicle to detect the alertness of the driver and, when necessary, this system can inform the emergency services to save the lives on the road. This research involved proposing and implementing a solution based on the iris detection mechanism to detect and notify the driver when impaired driving is detected. The implementation will not only help in saving thousands of lives lost in road mishaps but also in saving public property. The proposed approach is easy to install, inexpensive to afford and is very efficient in detection of a drowsy driver.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".