Summary MotionLM Multi-Agent Motion Forecasting as Language Modeling arxiv.org
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MotionLM is an advanced model that combines trajectory generation and interaction modeling to achieve top-notch performance in multi-agent motion forecasting for autonomous vehicles.
Slides
Slide Presentation (11 slides)
Key Points
- MotionLM is a model for multi-agent motion forecasting in autonomous vehicles.
- The model represents continuous trajectories as sequences of discrete motion tokens and treats motion prediction as a language modeling task.
- MotionLM achieves state-of-the-art performance on the Waymo Open Motion Dataset, ranking first in the interactive challenge.
- The model combines trajectory generation and interaction modeling in a single decoding process to capture interaction dependencies within trajectories.
- MotionLM utilizes temporal causality, conditional rollouts, and ensemble techniques to improve predictions.
- The model architecture consists of an encoder that processes scene elements and a trajectory decoder that generates sequences of motion tokens.
- MotionLM demonstrates competitive performance on marginal motion prediction and achieves state-of-the-art results on interactive motion prediction.
- The model leverages multi-agent rollouts and discrete motion tokens to capture the joint distribution over multimodal futures.
Summaries
21 word summary
MotionLM is a model for multi-agent motion forecasting in autonomous vehicles, achieving state-of-the-art performance by combining trajectory generation and interaction modeling.
60 word summary
MotionLM is a model for multi-agent motion forecasting in autonomous vehicles. It achieves state-of-the-art performance on the Waymo Open Motion Dataset, combining trajectory generation and interaction modeling. The model's performance is evaluated on the Waymo Open Motion Dataset interactive validation set, with optimization and implementation details provided. MotionLM surpasses previous methods in capturing multimodal future behavior and predicting agent interactions.
160 word summary
MotionLM is a model for multi-agent motion forecasting in autonomous vehicles that represents continuous trajectories as sequences of discrete motion tokens. It achieves state-of-the-art performance on the Waymo Open Motion Dataset, ranking first in the interactive challenge. The model combines trajectory generation and interaction modeling in a single decoding process, maximizing the log probability of token sequences among interacting agents. MotionLM achieves competitive performance on marginal motion prediction and state-of-the-art results on interactive motion prediction. The model's interactive attention frequency and the number of rollouts generated have a significant impact on performance. The paper presents optimization and implementation details, including the use of teacher forcing and various hyperparameters. The model's performance is evaluated on the Waymo Open Motion Dataset interactive validation set, with visualizations provided in the supplementary material. In conclusion, MotionLM provides an effective approach to multi-agent motion forecasting by treating it as a language modeling task, surpassing previous methods in capturing multimodal future behavior and predicting agent interactions.
396 word summary
MotionLM is a model for multi-agent motion forecasting in autonomous vehicles that treats motion prediction as a language modeling task. It represents continuous trajectories as sequences of discrete motion tokens and does not require anchors or explicit latent variable optimization. The model produces joint distributions over interactive agent futures in a single decoding process and enables temporally causal conditional rollouts. MotionLM achieves state-of-the-art performance on the Waymo Open Motion Dataset, ranking first in the interactive challenge.
To capture the likely maneuvers and responses of road agents, MotionLM represents continuous trajectories as discrete motion tokens and leverages sequence models to forecast their behavior. The model combines trajectory generation and interaction modeling in a single decoding process, maximizing the log probability of token sequences among interacting agents.
MotionLM is evaluated on the Waymo Open Motion Dataset, ranking second in soft mAP in the marginal prediction challenge and achieving a substantially improved miss rate compared to previous works. In the interactive prediction challenge, the model outperforms previous models, achieving a 6% relative improvement in mAP and a 3% relative improvement in miss rate.
The model architecture consists of an encoder that processes scene elements and a trajectory decoder that generates sequences of motion tokens. The scene encoder encodes information from various modalities, and the trajectory decoder generates discrete motion tokens for multiple agents in a temporally causal manner. MotionLM enforces temporal causality by preserving the order of token sampling.
MotionLM achieves competitive performance on marginal motion prediction and state-of-the-art results on interactive motion prediction. The model's interactive attention frequency and the number of rollouts generated have a significant impact on performance.
The paper introduces MotionLM as a method for interactive motion forecasting that captures the joint distribution over multimodal futures. The model establishes new state-of-the-art performance on the Waymo Open Motion Dataset interactive prediction challenge. The optimization details and implementation details of the model are presented, including the use of teacher forcing and various hyperparameters.
The evaluation of the model is done using various metrics, and the performance is evaluated on the Waymo Open Motion Dataset interactive validation set. Visualizations are provided in the supplementary material, showcasing the model's predictions in various scenarios.
In conclusion, MotionLM provides an effective and flexible approach to multi-agent motion forecasting by treating it as a language modeling task. The model surpasses previous methods in capturing multimodal future behavior and accurately predicting agent interactions.
647 word summary
MotionLM is a model for multi-agent motion forecasting in autonomous vehicles. It represents continuous trajectories as sequences of discrete motion tokens and treats motion prediction as a language modeling task. The model has several advantages: it does not require anchors or explicit latent variable optimization, it produces joint distributions over interactive agent futures in a single decoding process, and it enables temporally causal conditional rollouts. MotionLM achieves state-of-the-art performance on the Waymo Open Motion Dataset, ranking first in the interactive challenge.
Modern sequence models often use next-token prediction objectives without domain-specific assumptions. MotionLM leverages this approach by representing continuous trajectories as discrete motion tokens, similar to language model vocabularies. The goal is to capture the likely maneuvers and responses of road agents by using sequence models to forecast their behavior.
To address limitations in existing joint prediction approaches, MotionLM combines trajectory generation and interaction modeling in a single decoding process. The model is trained to maximize the log probability of token sequences among interacting agents. At inference time, joint trajectories are produced step-by-step, with interacting agents sampling tokens simultaneously. The model can be applied to various behavior prediction tasks, including marginal, joint, and conditional predictions.
The performance of MotionLM is evaluated on the Waymo Open Motion Dataset. In the marginal prediction challenge, the model ranks second in soft mAP and achieves a substantially improved miss rate compared to previous works. In the interactive prediction challenge, MotionLM outperforms previous models, achieving a 6% relative improvement in mAP and a 3% relative improvement in miss rate.
The model architecture consists of an encoder that processes scene elements and a trajectory decoder that generates sequences of motion tokens. The scene encoder encodes information from various modalities, such as roadgraph elements and traffic light states. The trajectory decoder generates discrete motion tokens for multiple agents in a temporally causal manner. The tokens are sampled from a learned distribution and combined with attention mechanisms to produce joint trajectories.
MotionLM enforces temporal causality by preserving the order of token sampling. This allows for conditional rollouts that resemble causal interventions. The model also utilizes rollout aggregation to identify underlying modes of the joint future distribution and estimate their probabilities. Ensembling is used to account for epistemic uncertainty and improve predictions.
In experiments, MotionLM achieves competitive performance on marginal motion prediction and state-of-the-art results on interactive motion prediction. The model's interactive attention frequency and the number of rollouts generated have a significant impact on performance, with higher frequencies and more rollouts leading to better predictions.
Overall, MotionLM provides an effective and flexible approach to multi-agent motion forecasting by treating it as a language modeling task. The model surpasses previous methods in capturing multimodal future behavior and accurately predicting agent interactions.
The paper introduces a method for interactive motion forecasting called MotionLM, which leverages multi-agent rollouts over discrete motion tokens to capture the joint distribution over multimodal futures. The model establishes new state-of-the-art performance on the WOMD interactive prediction challenge.
The paper presents the optimization details and implementation details of the model. It uses teacher forcing to train the model and utilizes various hyperparameters such as learning rate, number of layers, hidden size, and number of attention heads. Inference is performed using nucleus sampling with a top-p parameter of 0.95.
The evaluation of the model is done using various metrics such as miss rate, mAP, soft mAP, minADE, minFDE, and prediction overlap. The performance of the model is evaluated on the WOMD interactive validation set, and results are presented for different interactive attention frequencies and numbers of rollouts per replica. The model achieves good performance across all metrics.
Visualizations are provided in the supplementary material, showcasing the model's predictions in various scenarios. Examples are shown for marginal vs. joint predictions, marginal vs. conditional predictions, and temporally causal vs. acausal conditioning.
In conclusion, the paper presents MotionLM as a method for interactive motion forecasting that achieves state-of-the
853 word summary
MotionLM is a model for multi-agent motion forecasting in autonomous vehicles. It represents continuous trajectories as sequences of discrete motion tokens and treats motion prediction as a language modeling task. The model has several advantages: it does not require anchors or explicit latent variable optimization, it produces joint distributions over interactive agent futures in a single decoding process, and it enables temporally causal conditional rollouts. MotionLM achieves state-of-the-art performance on the Waymo Open Motion Dataset, ranking first in the interactive challenge.
Modern sequence models often use next-token prediction objectives without domain-specific assumptions. MotionLM leverages this approach by representing continuous trajectories as discrete motion tokens, similar to language model vocabularies. The goal is to capture the likely maneuvers and responses of road agents by using sequence models to forecast their behavior. Marginal predictions, which treat agent behavior as independent and ignore interaction dependencies, are insufficient for planning systems. Existing joint prediction approaches either separate marginal trajectory generation from interaction scoring or do not model temporal dependencies within trajectories.
To address these limitations, MotionLM combines trajectory generation and interaction modeling in a single decoding process. The model is trained to maximize the log probability of token sequences among interacting agents. At inference time, joint trajectories are produced step-by-step, with interacting agents sampling tokens simultaneously. The model can be applied to various behavior prediction tasks, including marginal, joint, and conditional predictions.
The performance of MotionLM is evaluated on the Waymo Open Motion Dataset. In the marginal prediction challenge, the model ranks second in soft mAP and achieves a substantially improved miss rate compared to previous works. In the interactive prediction challenge, MotionLM outperforms previous models, achieving a 6% relative improvement in mAP and a 3% relative improvement in miss rate.
The model architecture consists of an encoder that processes scene elements and a trajectory decoder that generates sequences of motion tokens. The scene encoder encodes information from various modalities, such as roadgraph elements and traffic light states. The trajectory decoder generates discrete motion tokens for multiple agents in a temporally causal manner. The tokens are sampled from a learned distribution and combined with attention mechanisms to produce joint trajectories.
MotionLM enforces temporal causality by preserving the order of token sampling. This allows for conditional rollouts that resemble causal interventions. The model also utilizes rollout aggregation to identify underlying modes of the joint future distribution and estimate their probabilities. Ensembling is used to account for epistemic uncertainty and improve predictions.
In experiments, MotionLM achieves competitive performance on marginal motion prediction and state-of-the-art results on interactive motion prediction. The model's interactive attention frequency and the number of rollouts generated have a significant impact on performance, with higher frequencies and more rollouts leading to better predictions.
Overall, MotionLM provides an effective and flexible approach to multi-agent motion forecasting by treating it as a language modeling task. The model surpasses previous methods in capturing multimodal future behavior and accurately predicting agent interactions.
The paper introduces a method for interactive motion forecasting called MotionLM, which leverages multi-agent rollouts over discrete motion tokens to capture the joint distribution over multimodal futures. The model establishes new state-of-the-art performance on the WOMD interactive prediction challenge.
The model uses a vocabulary of motion tokens and discretized delta action space to represent the possible actions of each agent. The sequence length for 8-second futures is 16 motion tokens per agent. The model utilizes a scene encoder for scene encoding and a trajectory decoder for autoregressively decoding motion token sequences.
The paper presents the optimization details and implementation details of the model. It uses teacher forcing to train the model and utilizes various hyperparameters such as learning rate, number of layers, hidden size, and number of attention heads. Inference is performed using nucleus sampling with a top-p parameter of 0.95.
The evaluation of the model is done using various metrics such as miss rate, mAP, soft mAP, minADE, minFDE, and prediction overlap. The performance of the model is evaluated on the WOMD interactive validation set, and results are presented for different interactive attention frequencies and numbers of rollouts per replica. The model achieves good performance across all metrics.
The paper also discusses ablations, scaling analysis, and latency analysis of the model. Ablations are performed to analyze the performance of the model with different interactive attention frequencies and numbers of rollouts. Scaling analysis is done to evaluate the performance of the model with different parameter counts. Latency analysis is performed to measure the inference latency of the model for different numbers of rollouts.
Visualizations are provided in the supplementary material, showcasing the model's predictions in various scenarios. Examples are shown for marginal vs. joint predictions, marginal vs. conditional predictions, and temporally causal vs. acausal conditioning.
In conclusion, the paper presents MotionLM as a method for interactive motion forecasting that achieves state-of-the-art performance on the WOMD interactive prediction challenge. The model leverages multi-agent rollouts and discrete motion tokens to capture the joint distribution over multimodal futures. The evaluation of the model demonstrates its effectiveness in generating accurate and diverse predictions. Future work includes leveraging the trained model in model-based planning frameworks and exploring distillation strategies from large autoregressive teachers.