LidNet: Boosting Perception and Motion Prediction from a Sequence of LIDAR Point Clouds for Autonomous Driving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autonomous driving is strongly contingent on perception and motion prediction for scene understanding. In this paper, we propose LIDAR Network (LidNet) to boost perception and motion prediction accuracy by redesigning MotionNet architecture. MotionNet is a new real-time encoder-decoder model that achieves joint perception and motion prediction at a pixel level. LidNet improves MotionNet performance by replacing every two spatial convolution layers in its encoder-decoder architecture with residual blocks and relies on average pooling rather than strided convolution for spatial reduction. In addition, we adjust the lateral skip connections linking encoders and decoders to result in a symmetric network. The global temporal maximum pooling layers on the lateral connections are replaced with temporal average pooling. Further, we introduce a center layer between the encoder-decoder architecture, with no spatial reduction applied at the lowest levels. Our extensive evaluation performed on the nuScenes dataset confirms that LidNet outperforms the state-of-the-art and operates in real-time.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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