A Smart IoT Enabled End-to-End 3D Object Detection System for Autonomous Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
Integration of advanced signal processing, image processing, deep learning, edge computing, and the Internet of Things (IoT) into vehicles allows intelligent automated vehicles to navigate autonomously in different environments. It is crucial for reliable and safe driving that an autonomous vehicle can accurately, effectively, and efficiently recognize, perceive, and observe the surrounding environments. Autonomous vehicles comprise advanced sensor technologies such as RGB cameras and LiDaR that produce an extensive data set in the form of RGB images and 3D measurement points, also recognized as a point cloud. It is necessary to understand and interpret collected data information efficiently and to identify other road users, such as pedestrians and vehicles. Thus, we introduced a smart IoT-enabled deep learning based end-to-end 3D object detection system that works in real-time, emphasizing autonomous driving situations. The detection model is based on YOLOv3; firstly, the model is utilized for 2D object detection and then modified for 3D object detection purposes. The presented model uses point cloud, and RGB image data as input and outputs detected bounding boxes with confidence scores and class labels. Experiments are carried out on the Lyft data set; results reveal that the YOLOv3 model achieves high accuracy and outperforms from other state-of-the-art detection models in terms of effectiveness and accuracy. The overall accuracy of the model is 96% and 97% for 2D and 3D object detection, respectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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