Hierarchical Transformers for Motion Forecasting Based on Inverse Reinforcement Learning
Bibliographic record
Abstract
Understanding and forecasting the future states of surrounding agents is crucial for ensuring safe and human-like decision-making for autonomous vehicles, which plays a pivotal role in the future of intelligent transportation systems. While data-driven methodologies, including recent advancements such as transformers and graph neural networks, have made significant progress, managing multimodality, achieving scene conformability, and providing human-like decision-making in future steps of the autonomy stack remains challenging. Unlike existing data-driven motion forecasting approaches that do not explicitly consider human-like motion planning, this work proposes learning from human (expert) behavior based on Inverse Reinforcement Learning (IRL). We design a novel Hierarchical Transformer-based Motion Forecasting framework, called HTMF, that learns the multimodal future motion distribution using maximum entropy IRL and leverages transformer-based learned occupancy grid maps as a context-aware approximation to the true distribution, enabling the generation of realistic motions. To assess the performance of the proposed approach, we conduct comprehensive experiments on large-scale real-world datasets across diverse challenging scenarios such as highways and unsignalized intersections. The results demonstrate that HTMF outperforms other benchmark motion forecasting methods in terms of prediction errors and in generating more plausible and human-like trajectories.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".