标题： 通过扩散组合在多样化场景中快速安全的轨迹规划 显示英文标题

标题： Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition

摘要： 安全轨迹规划在复杂环境中仍然是一个重大挑战，传统方法往往以牺牲计算效率为代价来保证安全性。 全面的障碍物建模可以提高安全性，但计算成本较高，而近似方法虽然更高效，但可能影响安全性。 为了解决这个问题，本文提出了一种基于状态扩散模型的快速且安全的轨迹规划框架。 仅利用低维车辆状态，扩散模型在保证足够无碰撞特性的同时实现了显著的推理效率。 通过组合扩散模型，所提出的框架可以在各种场景中安全泛化，在未见过的场景中也能规划无碰撞轨迹。 为进一步确保生成轨迹的安全性，提出了一种高效的基于规则的安全过滤器，该过滤器可以从候选轨迹中选择同时满足足够安全性和控制可行性的最优轨迹。 在已见和未见场景中，所提出的方法在保持高安全性和稳定性的同时实现了高效的推理时间。 在F1TENTH车辆上的评估进一步证明了所提出的方法在实际应用中的可行性。 项目页面为：https://rstp-comp-diffuser.github.io/。 显示英文摘要

摘要： Safe trajectory planning remains a significant challenge in complex environments, where traditional methods often trade off computational efficiency for safety. Comprehensive obstacle modeling improves safety but is computationally expensive, while approximate methods are more efficient but may compromise safety. To address this issue, this paper introduces a rapid and safe trajectory planning framework based on state-based diffusion models. Leveraging only low-dimensional vehicle states, the diffusion models achieve notable inference efficiency while ensuring sufficient collision-free characteristics. By composing diffusion models, the proposed framework can safely generalize across diverse scenarios, planning collision-free trajectories even in unseen scenes. To further ensure the safety of the generated trajectories, an efficient, rule-based safety filter is proposed, which selects optimal trajectories that satisfy both sufficient safety and control feasibility from among candidate trajectories. Both in seen and unseen scenarios, the proposed method achieves efficient inference time while maintaining high safety and stability. Evaluations on the F1TENTH vehicle further demonstrate that the proposed method is practical in real-world applications. The project page is at: https://rstp-comp-diffuser.github.io/.