DiffuSearch：混合轨迹规划如何受益于扩散空间与动作空间中对齐的目标
DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space
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- Robert Bosch GmbH（罗伯特·博世有限公司）
- University of Lübeck（吕贝克大学）
- University of Freiburg（弗赖堡大学）
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中文总结 AI 辅助
DiffuSearch是一种采用统一驾驶目标的混合轨迹规划器，结合引导式扩散模型与MCTS，在自动驾驶基准上实现了SOTA性能，有效减少碰撞并提升舒适性。
中文摘要 AI 辅助
在自动驾驶的轨迹规划中，混合规划架构通常由多个独立模块组成，每个模块有各自的目标。这种缺乏统一原则的情况会导致初始轨迹与优化后轨迹之间出现不一致，进而产生次优行为。针对这一问题，我们提出了DiffuSearch，这是一种新型混合规划器，在生成和优化阶段采用统一的目标集。我们的模型鼓励所有组件遵循相同的共享驾驶目标：避撞、可行驶区域合规性、舒适性和行驶进度。DiffuSearch采用两阶段架构：第一阶段，引导式扩散模型生成与场景一致的联合轨迹预测，将上述驾驶目标作为可微引导函数，隐式引导去噪过程；第二阶段，在离散动作空间中进行蒙特卡洛树搜索（MCTS），以相同的驾驶目标作为奖励函数，对初始轨迹进行显式局部优化。这种协同设计结合了扩散模型在寻找场景一致解决方案方面的优势，以及MCTS可解释、感知约束的优化能力。在nuPlan和interPlan反应式闭环基准上的实验表明，DiffuSearch表现强劲且常达SOTA性能，大幅减少碰撞、提升舒适性，尤其在复杂交互场景中效果显著。 ablation研究显示，MCTS优化是性能提升的主要机制，而隐式引导与显式搜索共享目标则进一步带来持续改进。
英文摘要
In trajectory planning for autonomous driving, hybrid planning architectures are often realized as a collection of disparate modules, each with its own objectives. This lack of a unifying principle can lead to inconsistencies between the initial and refined trajectory, resulting in suboptimal behavior. We address this by introducing DiffuSearch, a novel hybrid planner that uses a unified set of objectives across generation and refinement. Our model encourages all components to follow the same shared driving goals: collision avoidance, drivable area compliance, comfort, and progress. DiffuSearch employs a two-stage architecture. First, a guided diffusion model generates a scene-consistent, joint trajectory prediction, using our driving objectives as differentiable guidance functions to implicitly steer the denoising process. Second, a Monte Carlo Tree Search (MCTS) in a discretized action space performs an explicit, local refinement of this proposal, leveraging the same driving objectives as its reward function. This synergistic design leverages the diffusion model's strength in finding scene-consistent solutions combined with the explainable, constraint-aware refinement of MCTS. Experiments on nuPlan and interPlan reactive closed-loop benchmarks demonstrate that DiffuSearch achieves strong and often state-of-the-art performance, substantially reducing collisions and improving comfort, particularly in complex, interactive scenarios. Our ablation studies indicate that MCTS refinement is the main mechanism behind the gains, while sharing objectives between implicit guidance and explicit search provides further consistent improvements.
