LiCaNext: Incorporating Sequential Range Residuals for Additional Advancement in Joint Perception and Motion Prediction
Bibliographic record
Abstract
Autonomous driving can obtain accurate perception and reliable motion prediction with the support of multi-modal fusion. Recently, there has been growing interest in leveraging features from various onboard sensors to enhance the primary stages of autonomous driving. This paper proposes LiCaNext to capture additional accuracy advancements in joint perception and motion prediction while maintaining real-time requirements. LiCaNext is the next version of LiCaNet, which fuses LIDAR data in both bird’s-eye view (BEV) and range view (RV) representations with a camera image. In contrast to LiCaNet, we introduce sequential range residual images into the multi-modal fusion network to further improve performance, with minimal increase in inference time. Employing sequential range residual images has a substantial direct impact on motion prediction and positively influences perception. We provide an extensive evaluation on the public nuScenes dataset. Our experiments show that incorporating sequential range residuals secures significant performance gain, with monotonic progress for a larger number of exploited residuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".