Green-Tech CAV: Next Generation Computing for Traffic Sign and Obstacle Detection in Connected and Autonomous Vehicles
Bibliographic record
Abstract
In recent years, advanced technologies are used in automobiles, which make it simpler to drive on roads. However, driving is not trouble-free considering the unexpected hindrances and difficulty in recognizing traffic signs while driving at high speeds. Due to the advancement in technologies such as image recognition, several automated vehicles and techniques are developed to assist the drivers while driving. In this paper, an integrated intelligent system is designed using YOLO V3 neural architecture for recognizing the obstacles and visualizing the traffic sign through the input image from the camera of the moving vehicle. A Vehicular Adhoc Network (VANET) is being used with 5G connectivity to share the information to the other passing vehicles present in the vicinity. Apart from this, a notification is sent to the nearby centre for clearing any obstacle if found on roads during transportation. Real-time images of every possible obstacle such as a crack on roads along with traffic signs are trained using machine learning algorithms, thereby making the system more efficient and accurate. These techniques are developed in order to provide an pollution free environment and reduce accidents in roads with the concepts of green computing. The proposed automated system performs exemplary in recognizing the obstacle and traffic sign, making it easy for the drivers to drive more freely and reducing the pollution thereby producing a eco-friendly environment. According to the algorithms proposed in this paper, the results show that the algorithm used produces accurate results compared to the existing techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".