Enhancing the Collaborative Decision-Making Performance of Connected and Autonomous Vehicles: A Multi-Modal Failure-Aware Graph Representation Approach
Bibliographic record
Abstract
Recently, graph reinforcement learning (GRL)-based methods have demonstrated superior performance in solving decision-making issues. However, existing GRL-based methods encounter challenges in adequately integrating driving environment information and accurately capturing vehicular interactions. Resolving these issues is crucial for enhancing the safety and efficiency of intelligent transportation systems. To address these challenges, this paper presents a novel multi-modal failure-aware graph representation (MM-FA-GR) approach. The objective is to enhance the completeness and stability of graph representation techniques in the GRL-based setting, thereby improving the decision-making performance of CAVs operating in mixed autonomy traffic. Initially, a risk assessment invariant feature extractor (RA-IFE) is introduced to efficiently and selectively aggregate vehicle driving features into the node feature matrix. Subsequently, a multi-modal interaction model (MIM) is developed to comprehensively represent the mutual effects among vehicles and construct a multi-dimensional adjacency matrix. Moreover, a dynamic failure model (DFM) is incorporated to assess the sensing and communication failures of CAVs, enhancing the model’s robustness in non-ideal driving environments. Finally, a GRL model is established to solve the optimized driving strategies for CAVs. The proposed MM-FA-GR method has large potential to advance the graph representation technology, thereby enhancing decision-making performance in mixed autonomy traffic and improving robustness in non-ideal driving conditions. Comprehensive experiments are conducted with three typical traffic scenarios. Results show that our proposed MM-FA-GR method outperforms several baselines regarding safety, efficiency, and stability, highlighting the effectiveness of the core components within the proposed method. Moreover, the quantitative experiment validates the generalization capability of the proposed method in addressing the decision-making challenges of CAVs across diverse scenarios and traffic densities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".