CoBEVFusion Cooperative Perception with LiDAR-Camera Bird's Eye View Fusion
Bibliographic record
Abstract
Autonomous Vehicles (AVs) use multiple sensors to gather information about their surroundings. Connected Autonomous Vehicles (CAVs) share sensor data for increased safety and reliability through cooperative perception. However, most recent approaches in cooperative perception share unimodal information perceived using a single sensor such as only camera video data or only LiDAR point cloud data or perform multi-modal data fusion at the early or late stage. In this research, we explore vehicular perception utilizing intermediate fusion of mul-timodal camera video and LiDAR point cloud data. We propose the Dual Window-based Cross-Attention (DWCA) model which extracts and fuses selected camera and LiDAR data features and projects that onto a Bird's-Eye View (BEV) representation on a single ego vehicle. We demonstrate that using multimodal camera and LiDAR data, our DWCA model exceeds the performance of state-of-the-art (SOTA) models using unimodal data in vehicular object detection and segmentation tasks on a single vehicle. Next, we propose a model for Cooperative Perception, CoBEVFusion, which aggregates the fused BEV representations obtained from surrounding CAVs using a 3D Convolutional Neural Network. We validate our CoBEVFusion framework on the cooperative perception dataset, OPV2V, for two perception tasks: 3D object detection and BEV semantic segmentation. The CoBEVFusion model outperforms SOTA models in object detection tasks when using unimodal data or multimodal camera-LiDAR data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".