用辫子拓扑思想改进多智能体轨迹预测精度
SRefiner: Soft-Braid Attention for Multi-Agent Trajectory Refinement
- 基于软辫结构设计注意力机制，捕捉轨迹时空拓扑关系
- 在两个数据集上超越基线方法，实现新最佳性能
- 适合自动驾驶场景下轨迹精细化建模的研究者
准确预测多智能体未来轨迹对自动驾驶系统安全高效决策至关重要。轨迹精修已成为提升预测精度的关键策略。然而，现有方法常忽视轨迹间的拓扑关系，而这类关系对提高预测精度极为重要。受辫理论启发，我们提出一种新型轨迹精修方法——软辫精修器（SRefiner），通过软辫拓扑结构引导的软辫注意力机制实现。该注意力机制在‘软交叉点’处结合空间邻近性与车辆运动状态，捕捉轨迹间的时空拓扑关系。此外，我们还将该方法扩展至建模轨迹与车道之间的交互，进一步提升预测精度。SRefiner是一个多迭代、多智能体框架，通过迭代式精修并融入拓扑信息，增强交通场景中智能体间的交互建模能力。在两个数据集上，SRefiner显著优于四种基线方法，建立了轨迹精修的新基准。代码已开源：https://github.com/Liwen-Xiao/SRefiner。
原文摘要 · Abstract (English)
Accurate prediction of multi-agent future trajectories is crucial for autonomous driving systems to make safe and efficient decisions. Trajectory refinement has emerged as a key strategy to enhance prediction accuracy. However, existing refinement methods often overlook the topological relationships between trajectories, which are vital for improving prediction precision. Inspired by braid theory, we propose a novel trajectory refinement approach, Soft-Braid Refiner (SRefiner), guided by the soft-braid topological structure of trajectories using Soft-Braid Attention. Soft-Braid Attention captures spatio-temporal topological relationships between trajectories by considering both spatial proximity and vehicle motion states at ``soft intersection points". Additionally, we extend this approach to model interactions between trajectories and lanes, further improving the prediction accuracy. SRefiner is a multi-iteration, multi-agent framework that iteratively refines trajectories, incorporating topological information to enhance interactions within traffic scenarios. SRefiner achieves significant performance improvements over four baseline methods across two datasets, establishing a new state-of-the-art in trajectory refinement. Code is here https://github.com/Liwen-Xiao/SRefiner.
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