Enhancing BEV Perception Through Vehicle-Road Cooperative Systems: An Attention-Based Cross-View Fusion Approach
Bibliographic record
Abstract
This paper presents a novel vehicle-to-infrastructure (V2I) cooperative perception framework to address inherent limitations of bird's eye view (BEV) systems in autonomous driving. Using sensor constraints that include occlusion and restricted field of view, our framework integrates sensor systems mounted on the infrastructure with perception of the ego vehicle through three innovations. First, a geometry-aware feature alignment module that resolving cross-view discrepancies via projective geometry transformations. Second, attention-optimized fusion architecture (AFFENet) with dual-attention mechanisms for channel recalibration and spatial-contextual enhancement. Third, a multiscale dynamic aggregation protocol enabling context-aware fusion of heterogeneous sensor data. Evaluated on DAIR-V2X dataset, the framework achieves an improvement of 14. 03% mAP over baselines from BEV while reducing blind zone coverage by 30. 46% with real-time efficiency. This work establishes a new paradigm for cooperative perception systems, providing theoretical foundations and practical implementations for multiperspective sensor fusion in intelligent transportation ecosystems. The proposed methodologies address critical challenges in geometric alignment in cross-view and adaptive feature fusion, ultimately advancing robust autonomous driving systems through infrastructure-vehicle perception synergy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".