AMP：基于下一Token预测重新审视自动驾驶中的自回归运动预测
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
AI总结：
本文将GPT风格的下一Token预测引入自动驾驶运动预测，通过统一输入输出空间实现自回归预测，并提出三个分解注意力模块和不同位置编码以捕捉驾驶场景的时空与语义关系，在Waymo数据集上达到SOTA。
摘要：
作为自动驾驶（AD）中的一项基本任务，运动预测旨在预测周围物体的未来状态以进行导航。一种自然的解决方案是以逐步的方式估计其他智能体的位置，其中每个预测的时间步都以观察到的和先前预测的时间步为条件，即自回归预测。SocialLSTM和MFP等先驱工作基于这种直觉设计了它们的解码器。然而，几乎所有最先进的工作都假设所有预测的时间步在以观察到的为条件时是独立的，它们使用单个线性层同时生成所有时间步的位置。由于训练MLP的简单性，与自回归网络相比，它们主导了大多数运动预测排行榜。在本文中，我们将GPT风格的下一Token预测引入运动预测。这样，输入和输出可以在统一的空间中表示，从而使自回归预测更加可行。然而，不同于由同质单元（单词）组成的语言数据，驾驶场景中的元素可能具有复杂的时空和语义关系。为此，我们提出采用三个分解的注意力模块，针对不同的邻居进行信息聚合，并采用不同的位置编码风格来捕捉它们的关系，例如，为空间相对性编码坐标系之间的变换，同时为时间相对性采用RoPE。在经验上，通过配备上述定制设计，所提出的方法在Waymo Open Motion和Waymo Interaction数据集上实现了最先进的性能。值得注意的是，AMP优于其他最近的自回归运动预测方法：MotionLM和StateTransformer，这证明了所提出设计的有效性。
英文摘要：
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation. One natural solution is to estimate the position of other agents in a step-by-step manner where each predicted time-step is conditioned on both observed time-steps and previously predicted time-steps, i.e., autoregressive prediction. Pioneering works like SocialLSTM and MFP design their decoders based on this intuition. However, almost all state-of-the-art works assume that all predicted time-steps are independent conditioned on observed time-steps, where they use a single linear layer to generate positions of all time-steps simultaneously. They dominate most motion prediction leaderboards due to the simplicity of training MLPs compared to autoregressive networks. In this paper, we introduce the GPT style next token prediction into motion forecasting. In this way, the input and output could be represented in a unified space and thus the autoregressive prediction becomes more feasible. However, different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. To this end, we propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations, e.g., encoding the transformation between coordinate systems for spatial relativity while adopting RoPE for temporal relativity. Empirically, by equipping with the aforementioned tailored designs, the proposed method achieves state-of-the-art performance in the Waymo Open Motion and Waymo Interaction datasets. Notably, AMP outperforms other recent autoregressive motion prediction methods: MotionLM and StateTransformer, which demonstrates the effectiveness of the proposed designs.
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。
