MV-STGHAT: Multi-View Spatial-Temporal Graph Hybrid Attention Network for Decision-Making of Connected and Autonomous Vehicles
Bibliographic record
Abstract
Cooperative decision-making technology for connected and autonomous vehicles (CAVs) plays a crucial role in the advancement of autonomous driving. Recently, graph reinforcement learning (GRL)-based methods have shown impressive results in addressing decision-making challenges by utilizing graph-based technologies. However, existing GRL research still faces difficulties in fully modeling mixed traffic scenarios and effectively generating driving feature embeddings. To address these issues, a multi-view spatial-temporal graph hybrid attention network (MV-STGHAT) is proposed to improve the feature extraction ability of the neural network model in the GRL-based framework, thereby improving the decision-making performance of CAVs. Initially, a method for constructing a multi-view spatial-temporal graph is introduced to effectively represent the interactions amongst vehicles. Then, an MV-STGHAT model is proposed, which integrates a dynamic gated graph attention network (DG-GAT) and channel attention temporal convolutional network (TCN) to efficiently generate the informative feature embedding. Furthermore, the double deep Q-learning (DDQN) algorithm is employed to train the proposed MV-STGHAT model. Finally, extensive experiments are conducted across three representative traffic scenarios to validate the proposed approach. Results show that our proposed method achieves zero collision and outperforms the baselines in multiple critical evaluation metrics, with improvements of up to 55.61% in overall reward, 33.68% in average speed, 33.49% in traveling efficiency, and 88.48% in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".