Risk-aware multi-task hybrid-attention multi-agent reinforcement learning for decision-making at unsignalized intersections

Abstract

Multi-Agent Reinforcement Learning (MARL) has demonstrated significant potential for cooperative decision-making in connected and autonomous vehicles (CAVs). However, existing approaches often fail to address the task-specific characteristics and varying requirements of vehicles in high-dynamic, unsignalized intersection scenarios. In these environments, vehicles are frequently exposed to conflict zones where risks, such as collisions, are difficult to perceive, particularly in the absence of traffic signals. Additionally, current methods lack effective mechanisms for balancing learning performance across multiple tasks. To overcome these challenges, we propose a novel multi-task MARL framework tailored for unsignalized intersections. The framework incorporates a hybrid-attention network that captures the influence of surrounding vehicles on different driving tasks, improving multi-agent decision-making. A multi-task diversity priority sampling mechanism is introduced to prioritize high-quality episodes from more complex tasks, enhancing performance in dynamic intersection settings. Furthermore, a risk-aware local decision corrector optimizes decision-making in high-risk conflict zones by enabling vehicles to predict and adapt to surrounding traffic behaviors. The proposed framework is evaluated through simulations, demonstrating superior performance compared to state-of-the-art baselines. A miniature intelligent vehicle testbed further validates its effectiveness and potential for real-world deployment.