Multilevel-Attention-Driven Decision-Making Framework for Unsignalized Intersections Based on Dual-Buffer Soft Actor–Critic

A novel autonomous driving motion planning framework for unsignalized intersections is presented. To achieve an effective balance between safety and efficiency in the decision-making process, a motion planning decision strategy tailored for discrete action spaces is developed based on the discrete soft actor–critic algorithm. In response to the challenges posed by the complexity of feature information in dense intersection environments, a multilevel attention mechanism—integrating both feature-level and vehicle-entity-level information—is introduced to significantly enhance feature extraction and processing capabilities. Furthermore, to mitigate the issues of temporal sample distribution imbalance and low utilization of high-value samples in a single experience pool, a dual experience buffer prioritized replay mechanism is proposed, thereby improving training stability. Experimental results indicate that, compared with alternative methods, the proposed framework not only achieves a superior balance between efficiency and safety but also exhibits enhanced interpretability and generalization performance.