用注意力机制预测路口风险，让自动驾驶更安全高效。
Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
- 引入有偏注意力构建风险预测器，评估车辆入交口的长期碰撞风险。
- 通过密集奖励信号提升决策安全性，仿真中同时改善效率与安全。
- 适合研究自动驾驶决策、强化学习在交通场景应用的读者。
无信号灯交叉口的自动驾驶决策因动态交互复杂且冲突风险高而极具挑战。为实现主动安全控制，本文提出一种融合有偏注意力机制的深度强化学习（DRL）决策框架，基于软演员-评论家（SAC）算法。其核心创新在于利用有偏注意力构建交通风险预测器，评估车辆进入交叉口的长期碰撞风险，并将该风险转化为密集奖励信号，指导SAC智能体做出安全高效的驾驶决策。仿真结果表明，所提方法显著提升了交叉口的交通效率与车辆安全性，验证了该智能决策框架在复杂场景中的有效性。代码已公开于 https://github.com/hank111525/SAC-RWB。
原文摘要 · Abstract (English)
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.
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