End-to-End Multi-View Fusion for Enhanced Perception and Motion Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Perception and motion prediction are indispensable components to the smooth operation of autonomous vehicles and the safety of the surrounding environment. Strengthening the accuracy of perception and motion prediction in autonomous vehicles remains of paramount importance. Therefore, we propose an end-to-end multi-view fusion methodology applied to MotionNet backbone network to enhance the sharpness of both perception and motion prediction. MotionNet is a state-of-the-art real-time model designed for joint perception and motion prediction. Our multi-view input is based on a single LIDAR sensor and formed by the fusion of range view features with bird's eye view. We evaluate our proposed work on nuScenes dataset and demonstrate through experiments that our proposed extension to MotionNet using the multi-view fusion technique outperforms MotionNet in both perception and motion prediction, especially for small and distant objects.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 it