Traffic Object Detection and Recognition Based on the Attentional Visual Field of Drivers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traffic object detection and recognition systems play an essential role in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV). In this research, we focus on four important classes of traffic objects: traffic signs, road vehicles, pedestrians, and traffic lights. We first review the major traditional machine learning and deep learning methods that have been used in the literature to detect and recognize these objects. We provide a vision-based framework that detects and recognizes traffic objects inside and outside the attentional visual area of drivers. This approach uses the driver 3D absolute coordinates of the gaze point obtained by the combined, cross-calibrated use of a front-view stereo imaging system and a non-contact 3D gaze tracker. A combination of multi-scale HOG-SVM and Faster R-CNN-based models are utilized in the detection stage. The recognition stage is performed with a ResNet-101 network to verify sets of generated hypotheses. We applied our approach on real data collected during drives in an urban environment with the RoadLAB instrumented vehicle. Our framework achieved 91% of correct object detections and provided promising results in the object recognition stage.
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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 | 0.000 | 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 it