Design and Construction of a Vehicle Detection Device Based on Nodemcu Ultrasonic Sensor and Running Text as Information
Bibliographic record
Abstract
Driving safety is often compromised by limited visibility, particularly when behind large vehicles or on narrow roads and sharp bends, which can increase the risk of accidents due to inappropriate overtaking decisions. This research designed and built a vehicle detection device based on a NodeMCU ESP8266, an HC-SR04 ultrasonic sensor, and running text as an information medium. It is equipped with a Neo 6M GPS module and Telegram application integration as an IoT feature. The system works by detecting vehicles in front using an ultrasonic sensor. The NodeMCU then processes the data and displays the message "Overtaking is prohibited" or "Please Overtake" on the running text, while also sending the vehicle's location in real time via Telegram. The research used a prototype method with a waterfall model, starting from requirements analysis, design, implementation, and testing. Test results showed that the system is capable of providing clear visual information and accurate location notifications, thus assisting drivers in making safer decisions. However, several limitations were identified, including the ultrasonic sensor's instability in certain weather conditions, the GPS module's time-consuming signal acquisition in closed areas, and the running text's limited character set when configured directly through the program.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".