Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural\n Networks
Bibliographic record
Abstract
We investigate the multi-step prediction of the drivable space, represented\nby Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that\naccurate multi-step prediction of the drivable space can efficiently improve\npath planning and navigation resulting in safe, comfortable and optimum paths\nin autonomous driving. We train a variety of Recurrent Neural Network (RNN)\nbased architectures on the OGM sequences from the KITTI dataset. The results\ndemonstrate significant improvement of the prediction accuracy using our\nproposed difference learning method, incorporating motion related features,\nover the state of the art. We remove the egomotion from the OGM sequences by\ntransforming them into a common frame. Although in the transformed sequences\nthe KITTI dataset is heavily biased toward static objects, by learning the\ndifference between subsequent OGMs, our proposed method provides accurate\nprediction over both the static and moving objects.\n
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".