用人工桥接方法实现多约束扩散生成，提升自动驾驶路径规划能力
Constrained Generative Modeling with Manually Bridged Diffusion Models
- 通过人工桥接融合多重约束，保持扩散模型的数学有效性
- 在路径规划任务中成功生成符合物理与环境约束的轨迹
- 适合需要高可靠性轨迹生成的自动驾驶系统开发
本文提出一种基于扩散模型的约束空间生成建模新框架。核心是引入'人工桥接'机制，扩展可实用的约束类型以构建扩散桥接。我们设计了融合多种约束的方法，使最终的多约束模型仍为有效的人工桥接，并保持对所有约束的尊重。同时，开发了训练机制，使模型既能满足多重约束，又能匹配真实数据分布。理论部分证明了所提机制的数学合理性。实验展示了该方法在受限生成任务中的应用，特别强调其在自动驾驶车辆路径规划中初始轨迹生成的高价值应用。
原文摘要 · Abstract (English)
In this paper we describe a novel framework for diffusion-based generative modeling on constrained spaces. In particular, we introduce manual bridges, a framework that expands the kinds of constraints that can be practically used to form so-called diffusion bridges. We develop a mechanism for combining multiple such constraints so that the resulting multiply-constrained model remains a manual bridge that respects all constraints. We also develop a mechanism for training a diffusion model that respects such multiple constraints while also adapting it to match a data distribution. We develop and extend theory demonstrating the mathematical validity of our mechanisms. Additionally, we demonstrate our mechanism in constrained generative modeling tasks, highlighting a particular high-value application in modeling trajectory initializations for path planning and control in autonomous vehicles.
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