Enhancing the Collaborative Decision-Making Performance of Connected and Autonomous Vehicles: A Multi-Modal Failure-Aware Graph Representation Approach

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.