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arXiv · 2309.16534

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

Ari Seff·Brian Cera·Dian Chen·Mason Ng·Aurick Zhou·Nigamaa Nayakanti·Khaled S. Refaat·Rami Al-Rfou·Benjamin Sapp
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Abstract

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.

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Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp. 2023-09-28. MotionLM: Multi-Agent Motion Forecasting as Language Modeling. https://arxiv.org/abs/2309.16534

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