A Novel Transformer-Based Model for Motion Forecasting in Connected Automated Vehicles
Bibliographic record
Abstract
As the realms of Connected Automated Vehicles (CAVs) and the Internet of Vehicles develop, the ability to accurately forecast vehicle motions takes center stage in shaping the future of intelligent transportation systems, integrating vehicles harmoniously into a connected, data-driven ecosystem. Never-theless, vehicle motion forecasting for CAVs faces considerable challenges, including handling multimodal behavior and effectively considering the complex interactions between surrounding agents. To mitigate these challenges, we design an innovative Transformer-based model for Motion Forecasting (TMF) that takes into account the uncertainty in human driving behavior and the complex interactions between agents. More specifically, we explic-itly integrate map constraints by extracting agent-lane temporal and spatial interrelated features. Our transformer-based encoder benefits from an attention mechanism to enable social interactions, effectively acquiring meaningful representations of these scene elements to attain precise predictions. The evaluation results over the extensive Argoverse Motion Forecasting dataset demonstrate that TMF achieves higher performance when compared to several state-of-the-art models.
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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".