DeMo：将运动预测解耦为方向意图与动态状态
DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States
AI总结：
针对自动驾驶中交通智能体运动预测因缺乏轨迹细致表示导致效果不佳的问题，提出DeMo框架将轨迹查询解耦为方向意图模式查询与动态状态查询，结合Attention与Mamba技术优化，在两大基准上实现运动预测SOTA性能。
摘要：
对交通智能体进行准确的运动预测，是保障自动驾驶系统在动态变化环境中安全性与效率的关键。主流方法采用“单查询单轨迹”范式，即每个查询对应一条唯一轨迹，以此预测多模态轨迹。这类方法虽直接有效，但由于智能体状态随时间动态演化，缺乏对未来轨迹的细致表示可能导致结果欠佳。为解决该问题，我们提出DeMo框架，将多模态轨迹查询解耦为两类：捕捉不同方向意图的模式查询（mode queries），以及追踪智能体随时间动态状态的状态查询（state queries）。借助该形式，我们分别优化轨迹的多模态特性与动态演化特性，随后整合模式查询与状态查询，得到全面且细致的轨迹表示。为实现这些操作，我们还引入了结合Attention与Mamba的技术，分别利用二者优势进行全局信息聚合与状态序列建模。在Argoverse 2与nuScenes两个基准上的大量实验表明，我们的DeMo在运动预测任务上达到了当前最优性能。
英文摘要：
Accurate motion forecasting for traffic agents is crucial for ensuring the safety and efficiency of autonomous driving systems in dynamically changing environments. Mainstream methods adopt a one-query-one-trajectory paradigm, where each query corresponds to a unique trajectory for predicting multi-modal trajectories. While straightforward and effective, the absence of detailed representation of future trajectories may yield suboptimal outcomes, given that the agent states dynamically evolve over time. To address this problem, we introduce DeMo, a framework that decouples multi-modal trajectory queries into two types: mode queries capturing distinct directional intentions and state queries tracking the agent's dynamic states over time. By leveraging this format, we separately optimize the multi-modality and dynamic evolutionary properties of trajectories. Subsequently, the mode and state queries are integrated to obtain a comprehensive and detailed representation of the trajectories. To achieve these operations, we additionally introduce combined Attention and Mamba techniques for global information aggregation and state sequence modeling, leveraging their respective strengths. Extensive experiments on both the Argoverse 2 and nuScenes benchmarks demonstrate that our DeMo achieves state-of-the-art performance in motion forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
