LiCaNet: Further Enhancement of Joint Perception and Motion Prediction Based on Multi-Modal Fusion
Bibliographic record
Abstract
The safety and reliability of autonomous driving pivots on the accuracy of perception and motion prediction pipelines, which reckons primarily on the sensors deployed onboard. Slight confusion in perception and motion prediction can result in catastrophic consequences due to misinterpretation in later pipelines. Therefore, researchers have recently devoted considerable effort towards enhancing perception and motion prediction models. However, targeting pixel-wise joint perception and motion prediction using different sensor modalities are often ignored. In this paper, we push performance even further by leveraging a multi-modal fusion network. We propose a novel LIDAR Camera Network (LiCaNet) that achieves accurate pixel-wise joint perception and motion prediction in real-time. LiCaNet expands on our earlier fusion network by incorporating a camera image into the fusion of LIDAR sourced sequential bird’s-eye view (BEV) and range view (RV) images. We present a comprehensive evaluation using nuScenes dataset to validate the outstanding performance of LiCaNet compared to the state-of-the-art. Experiments reveal that utilizing a camera sensor results in a substantial gain in perception and motion prediction. Moreover, most of the improvements achieved fall within the camera range, with the highest registered for small and distant objects, confirming the significance of incorporating a camera sensor into a fusion network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".