Deep Learning-based Road Object Detection for Collision Avoidance in Autonomous Driving
Bibliographic record
Abstract
With the population in metro cities rising, traffic congestion has become a significant issue, resulting in an alarming number of traffic collisions and road accidents. Given the advancements in automated vehicles, it is crucial to have a vehicle detection system that is equipped to handle the various challenges that arise on the road, particularly when it comes to traffic collisions. Although many datasets are available to support object detection for traffic monitoring and management, the suitability of these datasets for specific weather conditions across the globe needs to be analyzed. We present Canadian vehicle datasets (CVD) and analyze the YOLOv8-based deep learning model using CVD. Thales, Canada, captured street-level videos in Quebec City, Canada, with a vehicle equipped with a high-quality RGB camera. The videos are taken day and night in all four seasons: rain, snow, hazy, and bright sunlight. We created 27378 labels for 11 classes for 8000 images. The YOLOv8 model trained on the publicly available roboFlow dataset was compared to the model trained on the combined roboflow and our weather-specific CVD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".