MotionLM：多智能体运动预测作为语言建模

摘要

在自动驾驶车辆的安全规划中，可靠地预测道路代理的未来行为是至关重要的组成部分。在这里，我们将连续轨迹表示为离散运动标记的序列，并将多代理运动预测构建为在该领域上的语言建模任务。我们的模型MotionLM具有几个优势：首先，它不需要锚点或显式潜在变量优化来学习多模态分布。相反，我们利用单一标准语言建模目标，最大化对序列标记的平均对数概率。其次，我们的方法绕过事后交互启发式，其中在交互评分之前进行单个代理轨迹生成。相反，MotionLM通过单一自回归解码过程在交互式代理未来上产生联合分布。此外，模型的序列分解使得时间因果条件展开成为可能。所提出的方法在Waymo Open Motion数据集上为多代理运动预测建立了新的最先进性能，位列交互式挑战排行榜第一。

English

Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain. Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable optimization to learn multimodal distributions. Instead, we leverage a single standard language modeling objective, maximizing the average log probability over sequence tokens. Second, our approach bypasses post-hoc interaction heuristics where individual agent trajectory generation is conducted prior to interactive scoring. Instead, MotionLM produces joint distributions over interactive agent futures in a single autoregressive decoding process. In addition, the model's sequential factorization enables temporally causal conditional rollouts. The proposed approach establishes new state-of-the-art performance for multi-agent motion prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive challenge leaderboard.

MotionLM：多智能体运动预测作为语言建模

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

摘要

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