A Low Cost Automated Accident Notification System: Design, Simulation and Experimental Results
Bibliographic record
Abstract
The last decades have seen the appearance and development of new embedded systems and IoT devices to improve car safety, such as auto brake, lane alignment, airbag, collision warning to name a few. However, despite the availability of new safety focused features, we still cannot put a break on increase in the number of car crash resulting in deaths. One of the main reasons for high death rate is late medical response to crash site or insufficient treatment of victims due to lack of knowledge of victim’s condition at the time of response. \nNational statistics clearly show that despite a growing wireless communication network and the availability of medical transport, the time to notify emergency personnel of a crash and respond to the crash victims can be quite lengthy. For fatal crashes in Canada, the average pre-hospital time is approximately 20 minutes in urban areas and 45 min to one hour in rural areas. \nIn this project, a new system was designed to notify the crash to health agencies and families via email, which is based on concepts of embedded system and IoT. There are several existing systems which also give the same results but the biggest asset of the proposed system is its ability for quick response with detail of seriousness of crash and image of the condition of victims in the car. Other features which similar system lack is the ability to communicate with each other. When we have lack of Internet due to no network on the phone or any other reason, systems of the crashed car will communicate with other systems in passing cars to relay the notification. In the project, two different kits were developed to work as two systems to test their performance and their ability to communicate with each other. The new system uses the hotspot feature of smartphone to provide Internet connection to send email when a crash occurs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".