Learnable Fusion Mechanisms for Object Detection in Autonomous Vehicles
Bibliographic record
Abstract
In this work, we propose a novel deep learning based sensor fusion framework, that uses both camera and LiDAR sensors in a multi-modal and multi-view setting. In order to leverage both data streams, we incorporate two new sophisticated fusion mechanisms: element-wise multiplication and multi-modal factorized bilinear pooling. When compared to previously used fusion operators such as element-wise addition and concatenation of feature maps, our proposed fusion methods significantly increase the bird’s eye view moderate average precision score by +4.97% and +8.35% for both methods, respectively, when evaluated on KITTI dataset for object detection. Furthermore, we provide a detailed study of important design choices that contribute to the performance of deep learning based sensor fusion frameworks such as data augmentation, multi-task learning, and the design of the convolutional architecture. Finally, we provide qualitative results that showcase both success and failure cases for our proposed framework. We also discuss directions for mitigating failure cases.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".