Vehicle Collision Warning Based on Combination of the YOLO Algorithm and the Kalman Filter in the Driving Assistance System
Bibliographic record
Abstract
Vehicle forward collision warning based on machine vision can help to reduce the incidence of traffic accidents. Many researchers have studied this topic in recent years. However, most of the existing studies only focus on one stage of the process such as vehicle detection and distance measurement. It will face many issues in practical application. To solve these problems, we propose a framework for forward collision warning. This study applies the YOLO algorithm to detect the vehicle and uses the Kalman filter to track the vehicle. The monocular vision distance measuring method is used to estimate the distance and travel speed. Finally, we adopt the time to collision (TTC) to decide whether to trigger the warning process. In the speed measurement stage, we design an appropriate time interval to calculate the relative speed of the front vehicle. In the collision warning segment, a TTC threshold is set by considering not only vehicle safety guarantees but also avoiding hard barking that would make drivers uncomfortable. Furthermore, we set a warning area to filter the false warning when the car overtakes and meets. Experiments with real traffic scenes demonstrate that the performance of the proposed model is good to make accurate collision prediction and warning.
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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 | 0.000 | 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".