Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-based methods are interpretable, their predefined heuristics lack the adaptability for dynamic traffic environments. Learning-based approaches have shown considerable promise. Conversely, learning-based approaches, despite their promise, struggle to balance the modeling diverse and multimodal driving behaviors and real-time planning, often leading to indecisive or unsafe actions. To address this limitation, we propose Consistency Planner, a real-time planning framework with fast-sampling consistency models. Our approach is built upon two key technical contributions. Efficient Multimodal Sampling: We employ fast-sampling consistency models to generate a diverse set of plausible future trajectories. This enables efficient, real-time exploration of multimodal actions, overcoming the computational bottlenecks of previous iterative generative methods. Heterogeneous Feature Fusion: We introduce an attention-enhanced decoder that dynamically integrates heterogeneous input features (including scene feature and action token) into a cohesive representation for robust planning. Extensive evaluation in the Waymax simulator demonstrates superior performance in safety metrics compared to existing methods, with particularly strong results in challenging dynamic scenarios.

翻译：在复杂真实驾驶场景中进行闭环规划，是自动驾驶系统面临的关键挑战。传统基于规则的方法虽具可解释性，但其预定义启发式策略难以适应动态交通环境。基于学习的方法展现出显著潜力，然而这类方法在平衡多模态驾驶行为建模与实时规划时存在局限，常导致决策犹豫或危险动作。针对该问题，我们提出Consistency Planner——一种基于快速采样一致性模型的实时规划框架。本研究包含两大关键技术贡献：第一，高效多模态采样：采用快速采样一致性模型生成多样化未来轨迹，在克服迭代生成式计算瓶颈的同时，实现多模态动作的高效实时探索；第二，异构特征融合：提出注意力增强解码器，通过动态整合场景特征与动作令牌等异构输入特征形成统一表示，实现鲁棒规划。在Waymax模拟器上的大量评估表明，本方法在安全指标上优于现有方法，尤其在挑战性动态场景中表现突出。