A Camera-LiDAR Fusion Framework for Traffic Monitoring
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) enabled with LiDAR and sensor fusion technology can provide critical information to increase road safety. Traffic monitoring systems can enable these changes through the collection of accurate and real-time detection and tracking information, but require consistent performance in all environmental conditions. Recent developments in LiDAR technology have opened new opportunities in autonomous vehicles, smart infrastructure, and surveillance. With high 3D accuracy and performance across adverse lighting and weather conditions compared to cameras alone, LiDAR stands to become commonplace in ITS applications. To further traffic monitoring systems, sensor fusion leverages complementary characteristics from multiple sensors to enhance detection performance in challenging conditions. However, many existing perception frameworks require prohibitively expensive computer hardware for widespread deployment and to achieve real-time performance. This paper presents the following contributions (1) a real-time multi-object detection and tracking pipeline using camera-LiDAR fusion, (2) Center for Mechatronics and Hybrid Technologies (CMHT) Traffic Dataset containing synchronized camera and LiDAR data. The proposed fusion framework is evaluated on the real-world CMHT Traffic Dataset, achieving a +3 Higher Order Tracking (HOTA) score compared to LiDAR or camera only.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".