Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance competing objectives and lack of safety assurance,due to limited adaptability and inadequacy in learning complex multi-modal behaviors commonly exhibited in human planning, not to mention their strong reliance on the fallback strategy with predefined rules. We propose a novel transformer-based Diffusion Planner for closed-loop planning, which can effectively model multi-modal driving behavior and ensure trajectory quality without any rule-based refinement. Our model supports joint modeling of both prediction and planning tasks under the same architecture, enabling cooperative behaviors between vehicles. Moreover, by learning the gradient of the trajectory score function and employing a flexible classifier guidance mechanism, Diffusion Planner effectively achieves safe and adaptable planning behaviors. Evaluations on the large-scale real-world autonomous planning benchmark nuPlan and our newly collected 200-hour delivery-vehicle driving dataset demonstrate that Diffusion Planner achieves state-of-the-art closed-loop performance with robust transferability in diverse driving styles.

翻译：在复杂开放世界环境中实现类人驾驶行为是自动驾驶领域的关键挑战。当前基于学习的规划方法（如模仿学习方法）由于适应性有限且难以学习人类规划中常见的复杂多模态行为，往往难以平衡相互冲突的目标并缺乏安全保障，更不用说这些方法严重依赖基于预定义规则的备用策略。我们提出了一种基于Transformer的扩散规划器用于闭环规划，该模型能够有效建模多模态驾驶行为并确保轨迹质量，无需任何基于规则的优化。我们的模型支持在同一架构下对预测和规划任务进行联合建模，从而实现车辆间的协同行为。此外，通过学习轨迹评分函数的梯度并采用灵活的类别引导机制，扩散规划器能够有效实现安全且适应性强的规划行为。在大规模真实世界自动驾驶规划基准nuPlan和我们新收集的200小时配送车辆驾驶数据集上的评估表明，扩散规划器在不同驾驶风格中均实现了最先进的闭环性能，并展现出强大的可迁移性。