LANet: A Lane Boundaries-Aware Approach for Robust Trajectory Prediction
Bibliographic record
Abstract
Accurate motion forecasting is critical for safe and efficient autonomous driving, enabling vehicles to predict future trajectories and make informed decisions in complex traffic scenarios. Most of the current designs of motion prediction models are based on the major representation of lane centerlines, which limits their capability to capture critical road environments and traffic rules and constraints. In this work, we propose an enhanced motion forecasting model informed by multiple vector map elements, including lane boundaries and road edges, that facilitates a richer and more complete representation of driving environments. An effective feature fusion strategy is developed to merge information in different vector map components, where the model learns holistic information on road structures and their interactions with agents. Since encoding more information about the road environment increases memory usage and is computationally expensive, we developed an effective pruning mechanism that filters the most relevant map connections to the target agent, ensuring computational efficiency while maintaining essential spatial and semantic relationships for accurate trajectory prediction. Overcoming the limitations of lane centerline-based models, our method provides a more informative and efficient representation of the driving environment and advances the state of the art for autonomous vehicle motion forecasting. We verify our approach with extensive experiments on the Argoverse 2 motion forecasting dataset, where our method maintains competitiveness on AV2 while achieving improved performance.
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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".