Imagining The Road Ahead: Multi-Agent Trajectory Prediction via\n Differentiable Simulation
Bibliographic record
Abstract
We develop a deep generative model built on a fully differentiable simulator\nfor multi-agent trajectory prediction. Agents are modeled with conditional\nrecurrent variational neural networks (CVRNNs), which take as input an\nego-centric birdview image representing the current state of the world and\noutput an action, consisting of steering and acceleration, which is used to\nderive the subsequent agent state using a kinematic bicycle model. The full\nsimulation state is then differentiably rendered for each agent, initiating the\nnext time step. We achieve state-of-the-art results on the INTERACTION dataset,\nusing standard neural architectures and a standard variational training\nobjective, producing realistic multi-modal predictions without any ad-hoc\ndiversity-inducing losses. We conduct ablation studies to examine individual\ncomponents of the simulator, finding that both the kinematic bicycle model and\nthe continuous feedback from the birdview image are crucial for achieving this\nlevel of performance. We name our model ITRA, for "Imagining the Road Ahead".\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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".