GMP: Goal-Based Multimodal Motion Prediction for Automated Vehicles
Bibliographic record
Abstract
To reliably and safely navigate dynamic urban environments, connected automated vehicles should anticipate the future motion of surrounding traffic agents, which can have fundamental implications on road safety, traffic management, and network communications in vehicular networks. This requires considering the inherent uncertainty in agents' behavior, making motion prediction challenging. To tackle this issue, we propose conditioning the agents' future motions on both context information and potential multimodal goals. We design a novel Goal-based Motion Prediction approach (GMP) for multimodal motion prediction. By encoding both interactions between agents using temporal convolutions and dynamic and static context information using graph attention, our method estimates the distribution of target goals, efficiently takes the inherent uncertainty in the behavior of agents into account, and generates precise multimodal trajectories. Experimental results indicate that GMP outperforms several benchmarks on the Argoverse Motion Forecasting dataset.
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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 | 0.000 |
| 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".