SIF-STGDAN: A Social Interaction Force Spatial-Temporal Graph Dynamic Attention Network for Decision-Making of Connected and Autonomous Vehicles
Bibliographic record
Abstract
The collaborative decision-making technology of connected and autonomous vehicles (CAVs) is critical in today’s autonomous driving. Recently, graph reinforcement learning (GRL)-based methods have demonstrated exemplary performance in solving decision-making problems by implementing graphic technologies. However, current GRL-based research faces the challenge of modeling the interaction completely and extracting driving features efficiently. To address these issues, this paper proposes a social interaction force (SIF) spatial-temporal graph dynamic attention network (SIF-STGDAN) to solve the decision-making of CAVs. First, a SIF model is established to better represent the mutual effect between vehicles; an on-ramp merging scenario is then constructed and modeled by graph representation. Then, the SIF-STGDAN is proposed by combining the temporal convolutional network (TCN) and graph dynamic attention network to extract the graphic features of the on-ramp scenario efficiently, and the double deep q-learning (DDQN) algorithm is utilized to generate the optimized driving strategies for CAVs. Finally, experiments are conducted, and results show that our proposed SIF-STGDAN outperforms the baselines in terms of safety, efficiency, and model stability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".