Recent advances in closed-loop planning benchmarks have significantly improved the evaluation of autonomous vehicles. However, existing benchmarks still rely on rule-based reactive agents such as the Intelligent Driver Model (IDM), which lack behavioral diversity and fail to capture realistic human interactions, leading to oversimplified traffic dynamics. To address these limitations, we present nuPlan-R, a new reactive closed-loop planning benchmark that integrates learning-based reactive multi-agent simulation into the nuPlan framework. Our benchmark replaces the rule-based IDM agents with noise-decoupled diffusion-based reactive agents and introduces an interaction-aware agent selection mechanism to ensure both realism and computational efficiency. Furthermore, we extend the benchmark with two additional metrics to enable a more comprehensive assessment of planning performance. Extensive experiments demonstrate that our reactive agent model produces more realistic, diverse, and human-like traffic behaviors, leading to a benchmark environment that better reflects real-world interactive driving. We further reimplement a collection of rule-based, learning-based, and hybrid planning approaches within our nuPlan-R benchmark, providing a clearer reflection of planner performance in complex interactive scenarios and better highlighting the advantages of learning-based planners in handling complex and dynamic scenarios. These results establish nuPlan-R as a new standard for fair, reactive, and realistic closed-loop planning evaluation. We will open-source the code for the new benchmark.

翻译：近期闭环规划基准的进展显著提升了自动驾驶车辆的评估水平。然而，现有基准仍依赖于基于规则的反应式智能体（如智能驾驶员模型IDM），其行为多样性不足且难以捕捉真实的人类交互，导致交通动态过度简化。为应对这些局限，我们提出了nuPlan-R，一种新型反应式闭环规划基准，它将基于学习的反应式多智能体仿真集成至nuPlan框架中。该基准以噪声解耦的扩散基反应式智能体替代基于规则的IDM智能体，并引入交互感知的智能体选择机制，以确保真实性与计算效率。此外，我们通过新增两项评估指标扩展了基准体系，以实现更全面的规划性能评估。大量实验表明，我们的反应式智能体模型能生成更真实、多样且类人的交通行为，从而构建出更能反映现实世界交互驾驶的基准环境。我们进一步在nuPlan-R基准中复现了一系列基于规则、基于学习及混合规划方法，更清晰地展现了规划器在复杂交互场景中的性能，并更好凸显了基于学习的规划器在处理复杂动态场景时的优势。这些成果确立了nuPlan-R作为公平、反应式且贴近现实的闭环规划评估新标准。我们将开源该新基准的代码。