Autonomous driving decision-making at unsignalized intersections is highly challenging due to complex dynamic interactions and high conflict risks. To achieve proactive safety control, this paper proposes a deep reinforcement learning (DRL) decision-making framework integrated with a biased attention mechanism. The framework is built upon the Soft Actor-Critic (SAC) algorithm. Its core innovation lies in the use of biased attention to construct a traffic risk predictor. This predictor assesses the long-term risk of collision for a vehicle entering the intersection and transforms this risk into a dense reward signal to guide the SAC agent in making safe and efficient driving decisions. Finally, the simulation results demonstrate that the proposed method effectively improves both traffic efficiency and vehicle safety at the intersection, thereby proving the effectiveness of the intelligent decision-making framework in complex scenarios. The code of our work is available at https://github.com/hank111525/SAC-RWB.

翻译：无信号交叉口的自动驾驶决策因复杂的动态交互和高冲突风险而极具挑战性。为实现主动安全控制，本文提出一种融合偏置注意力机制的深度强化学习决策框架。该框架基于Soft Actor-Critic算法构建，其核心创新在于利用偏置注意力构建交通风险预测器。该预测器评估车辆进入交叉口的长期碰撞风险，并将此风险转化为密集奖励信号，以引导SAC智能体做出安全高效的驾驶决策。仿真结果表明，所提方法能有效提升交叉口通行效率与车辆安全性，从而验证了该智能决策框架在复杂场景下的有效性。相关代码已发布于https://github.com/hank111525/SAC-RWB。