Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions
Why this work is in the frame
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Bibliographic record
Abstract
Detecting objects in 3D under various (normal and adverse) weather conditions is essential for safe autonomous driving systems. Recent approaches have focused on employing weather-insensitive 4D radar sensors and leveraging them with other modalities, such as LiDAR. However, they fuse multi-modal information without considering the sensor characteristics and weather conditions, and lose some height information which could be useful for localizing 3D objects. In this paper, we propose a novel framework for robust LiDAR and 4D radar-based 3D object detection. Specifically, we propose a 3D-LRF module that considers the distinct patterns they exhibit in 3D space (e.g., precise 3D mapping of LiDAR and wide-range, weather-insensitive measurement of 4D radar) and extract fusion features based on their 3D spatial relationship. Then, our weather-conditional radar-flow gating network modulates the information flow of fusion features depending on weather conditions, and obtains enhanced feature that effectively incorporates the strength of two domains under various weather conditions. The extensive experiments demonstrate that our model achieves SoTA performance for 3D object detection under various weather conditions.
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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 | 0.000 |
| 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