V2VFormer: Vehicle-to-Vehicle Cooperative Perception With Spatial-Channel Transformer
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Bibliographic record
Abstract
Collaborative perception aims for a holistic perceptive construction by leveraging complementary information from nearby connected automated vehicle (CAV), thereby endowing the broader probing scope. Nonetheless, how to aggregate individual observation reasonably remains an open problem. In this paper, we propose a novel vehicle-to-vehicle perception framework dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V2VFormer</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tr</i> ansformer-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</i> llaboration ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoTr</i> ). Specifically. it re-calibrates feature importance according to position correlation via Spatial-Aware Transformer ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAT</i> ), and then performs dynamic semantic interaction with Channel-Wise Transformer ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CWT</i> ). Of note, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoTr</i> is a light-weight and plug-in-play module that can be adapted seamlessly to the off-the-shelf 3D detectors with an acceptable computational overhead. Additionally, a large-scale cooperative perception dataset V2V-Set is further augmented with a variety of driving conditions, thereby providing extensive knowledge for model pretraining. Qualitative and quantitative experiments demonstrate our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V2VFormer</i> achieves the state-of-the-art (SOTA) collaboration performance in both simulated and real-world scenarios, outperforming all counterparts by a substantial margin. We expect this would propel the progress of networked autonomous-driving research in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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