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 in turn 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 developing accurate perception and motion prediction models. To that end, we propose LIDAR Camera network (LiCaNet) that leverages multi-modal fusion to further enhance the joint perception and motion prediction performance accomplished in our earlier work. LiCaNet expands on our previous fusion network by adding a camera image to the fusion of RV image with historical BEV data sourced from a LIDAR sensor. We present a comprehensive evaluation 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 perception gain over our previous fusion network and a steep reduction in displacement errors. Moreover, the majority of the achieved improvement falls within 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 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 | 0.000 | 0.001 |
| 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 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".