MotionLM：将多智能体运动预测视为语言建模
MotionLM: Multi-Agent Motion Forecasting as Language Modeling
AI总结：
本文提出 MotionLM，将多智能体连续轨迹离散为 token 并以标准语言建模目标自回归生成联合未来分布，在 Waymo 交互式运动预测挑战中取得第一。
摘要：
对道路智能体未来行为进行可靠预测，是自动驾驶汽车安全规划的关键组成部分。本文将连续轨迹表示为离散运动 token 序列，并把多智能体运动预测转化为该领域上的语言建模任务。我们的模型 MotionLM 具有若干优势：首先，它不需要锚点或显式潜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，最大化序列 token 的平均对数概率。其次，该方法绕过了先生成单个智能体轨迹、再进行交互式评分的事后交互启发式方法；MotionLM 通过单个自回归解码过程生成交互式智能体未来的联合分布。此外，模型的序贯分解支持时间因果的条件推演。所提方法在 Waymo Open Motion Dataset 上的多智能体运动预测任务中确立了新的最先进性能，并在交互式挑战赛排行榜上排名第一。
英文摘要：
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.
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。
