A Social-Aware Vehicle Path Forecasting Method using Graph Neural Networks
Bibliographic record
Abstract
Situational awareness can help the safety of automated vehicles, which involves understanding and forecasting the motions of nearby road users. Accurate motion forecasting enhances vehicular commutations, road safety, and mobility management. Early approaches merely model vehicle kinematics and ignore the impacts of nearby agents on each other, leading to inefficient results, especially for long predictions. Various types of agents use the same paths in a driving scenario. However, not all of these agents interact with each other. In fact, the actions of one agent in a road section do not impact all agents that use the same section. Accordingly, in this work, we argue that although modeling social interactions among road users is critical to have a safe path forecasting model, it should not be assumed that there is a connection between an agent and all of its nearby agents. We introduce a novel path forecasting model which benefits from graph neural networks to reason about these connections in terms of both time and distance. We produce the final predictions with temporal convolutions. We validate the path forecasting performance of our model using two large motion prediction benchmarks with different scenes and achieve state-of-the-art results in terms of displacement errors.
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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 | 0.001 |
| 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".