3D Objects Detection and Recognition from Color and LiDAR Data for Autonomous Driving
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
In recent years, autonomous driving vehicles are attracting growing commercial and scientific attention. How to detect and recognize objects in a complex real-world road environment represents one of the most important problems facing autonomous vehicles and their ability to make decisions on the road and in real time. While color imaging remains a rich source of information, LiDAR scanners can collect high quality data under different lighting conditions and can provide high-range and high-precision spatial information. Expanding object detection by processing simultaneously data collected by a color camera and a LiDAR scanner brings new capabilities to the field of autonomous driving. In this paper, a 3D object detector is proposed with focal loss and Euler angle regression to optimize the detector’s performance. It uses a bird’s-eye view map generated from a LiDAR point cloud and RGB images as input. Results show that the proposed 3D object detector reaches a speed over 46 frames per second and an average precision over 90%. In addition, a more compact detector is also proposed that processes the same input data three times faster with only slightly lower accuracy.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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