MotionDiffuser：使用擴散實現可控多智能體運動預測

摘要

我們提出了MotionDiffuser，這是一種基於擴散的表示方法，用於描述多個智慧體未來軌跡的聯合分佈。這種表示方法具有幾個關鍵優勢：首先，我們的模型學習到了一個能夠捕捉多樣未來結果的高度多模態分佈。其次，簡單的預測器設計僅需要單一的L2損失訓練目標，並且不依賴軌跡錨點。第三，我們的模型能夠以置換不變的方式學習多個智慧體運動的聯合分佈。此外，我們利用主成分分析（PCA）實現了壓縮的軌跡表示，從而提高了模型性能並實現了對精確樣本對數概率的高效計算。隨後，我們提出了一個通用的受限取樣框架，基於可微成本函數實現了受控軌跡取樣。這種策略可以實現一系列應用，例如強制執行規則和物理先驗，或創建定制的模擬場景。MotionDiffuser可以與現有的骨幹架構結合，以實現頂尖的運動預測結果。我們在Waymo Open Motion數據集上獲得了多智慧體運動預測的最新成果。

English

We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimodal distribution that captures diverse future outcomes. Second, the simple predictor design requires only a single L2 loss training objective, and does not depend on trajectory anchors. Third, our model is capable of learning the joint distribution for the motion of multiple agents in a permutation-invariant manner. Furthermore, we utilize a compressed trajectory representation via PCA, which improves model performance and allows for efficient computation of the exact sample log probability. Subsequently, we propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. This strategy enables a host of applications such as enforcing rules and physical priors, or creating tailored simulation scenarios. MotionDiffuser can be combined with existing backbone architectures to achieve top motion forecasting results. We obtain state-of-the-art results for multi-agent motion prediction on the Waymo Open Motion Dataset.

MotionDiffuser：使用擴散實現可控多智能體運動預測

MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion

摘要

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