MotionLM: Multi-Agent Motion Forecasting as Language Modeling

• URL: http://arxiv.org/abs/2309.16534v1
• Date: Thu, 28 Sep 2023 15:46:25 GMT
• Title: MotionLM: Multi-Agent Motion Forecasting as Language Modeling
• Authors: Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp
• Abstract summary: We present MotionLM, a language model for multi-agent motion prediction. Our approach bypasses post-hoc interactions where individual agent trajectory generation is conducted prior to interactive scoring. The model's sequential factorization enables temporally causal conditional rollouts.
• Score: 15.317827804763699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models [10.057312592344507]
  We propose a novel framework based on diffusion models, called SceneDM, to generate joint and consistent future motions of all the agents in a scene. SceneDM achieves state-of-the-art results on the Sim Agents Benchmark.
  arXiv  Detail & Related papers  (2023-11-27T11:39:27Z)
• MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying [110.83590008788745]
  Motion prediction is crucial for autonomous driving systems to understand complex driving scenarios and make informed decisions. In this paper, we propose Motion TRansformer (MTR) frameworks to address these challenges. The initial MTR framework utilizes a transformer encoder-decoder structure with learnable intention queries. We introduce an advanced MTR++ framework, extending the capability of MTR to simultaneously predict multimodal motion for multiple agents.
  arXiv  Detail & Related papers  (2023-06-30T16:23:04Z)
• MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion [15.146808801331774]
  MotionDiffuser is a diffusion based representation for the joint distribution of future trajectories over multiple agents. We propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. We obtain state-of-the-art results for multi-agent motion prediction on the Open Motion dataset.
  arXiv  Detail & Related papers  (2023-06-05T17:55:52Z)
• JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving [12.460224193998362]
  We propose an end-to-end trainable model that learns directly the interaction between pairs of agents in a structured, graphical model formulation. Our approach improves significantly on the trajectory overlap metrics while obtaining on-par or better performance on single-agent trajectory metrics.
  arXiv  Detail & Related papers  (2022-12-16T20:59:21Z)
• Motion Transformer with Global Intention Localization and Local Movement Refinement [103.75625476231401]
  Motion TRansformer (MTR) models motion prediction as the joint optimization of global intention localization and local movement refinement. MTR achieves state-of-the-art performance on both the marginal and joint motion prediction challenges.
  arXiv  Detail & Related papers  (2022-09-27T16:23:14Z)
• Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction [81.94175022575966]
  We introduce the task of action-driven human motion prediction. It aims to predict multiple plausible future motions given a sequence of action labels and a short motion history.
  arXiv  Detail & Related papers  (2022-05-31T08:38:07Z)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding [46.52703817997932]
  Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians. We propose a model that synthesizes multiple input signals from the multimodal world. We show a significant performance improvement over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-03-06T13:59:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.