GSAN: Graph Self-Attention Network for Learning Spatial–Temporal Interaction Representation in Autonomous Driving
Bibliographic record
Abstract
Modeling interactions among vehicles is critical in improving the efficiency and safety of autonomous driving since complex interactions are ubiquitous in many traffic scenarios. To model interactions under different traffic scenarios, most existing works consider interaction information implicitly in their specific tasks with hand-crafted features and predefined maneuvers. Extracting interaction representation, which can be commonly used among different downstream tasks, is not explored. In this article, we propose a general and novel graph self-attention network (GSAN) to learn the spatial–temporal interaction representation among vehicles by a framework consisting of pretraining and fine-tuning. Specifically, in the pretraining step, we construct the GSAN module based on a graph self-attention layer and a gated recurrent unit layer, and use trajectory autoregression to learn the interaction information among vehicles. In the fine-tuning step, we propose two different adaptation schemes to utilize the learned interaction information in various downstream tasks and fine-tune the entire model with only a few steps. To illustrate the effectiveness and generality of our spatial–temporal interaction model, we conduct extensive experiments on two typical interaction-related tasks, namely, lane-changing classification and trajectory prediction. The experiment results demonstrate that our approach significantly outperforms the state-of-the-art solutions of these two tasks. We also visualize the impact of surrounding vehicles on the ego vehicle in different interaction scenes. The visualization offers an intuitive explanation on how our model captures the dynamic changing interactions among vehicles and makes good predictions in various interaction-related tasks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".