MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion (2306.03083v1)

Abstract: We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimodal distribution that captures diverse future outcomes. Second, the simple predictor design requires only a single L2 loss training objective, and does not depend on trajectory anchors. Third, our model is capable of learning the joint distribution for the motion of multiple agents in a permutation-invariant manner. Furthermore, we utilize a compressed trajectory representation via PCA, which improves model performance and allows for efficient computation of the exact sample log probability. Subsequently, we propose a general constrained sampling framework that enables controlled trajectory sampling based on differentiable cost functions. This strategy enables a host of applications such as enforcing rules and physical priors, or creating tailored simulation scenarios. MotionDiffuser can be combined with existing backbone architectures to achieve top motion forecasting results. We obtain state-of-the-art results for multi-agent motion prediction on the Waymo Open Motion Dataset.

• The paper proposes a diffusion-based framework that predicts joint trajectories for multiple agents without relying on trajectory anchors.
• It leverages a transformer-based denoiser and PCA-driven latent space to efficiently model multimodal interactions under uncertainty.
• Empirical validation on the Waymo Open Motion Dataset shows state-of-the-art performance, surpassing existing methods in key metrics.

Controllable Multi-Agent Motion Prediction Using Diffusion Models

The paper introduces a sophisticated approach for multi-agent motion prediction leveraging diffusion models. The main contribution is the development of a framework, termed “\ours{}”, which models the joint distribution of future trajectories over multiple agents in a scene. This paper addresses significant challenges in the field of autonomous vehicle systems, namely, making robust and reliable predictions of possible future trajectories of various agents, while considering the uncertainty and multimodal nature of real-world interactions.

Overview of \ours{}

\ours{} utilizes diffusion models for predictive modeling, which have traditionally been effective in generative tasks such as image and video generation. The model is capable of learning a highly multimodal distribution, capturing diverse potential future outcomes in multi-agent scenarios. It eschews the need for trajectory anchors, relying instead on a simple L2 loss for training. Moreover, by adopting a permutation-invariant architecture, it efficiently learns joint distributions that account for interactions between multiple agents, which is crucial in autonomous driving contexts.

Technical Innovations

1. Transformer-Based Denoiser: The paper proposes a transformer-based architecture for the denoiser which is integral to the diffusion process. This architecture allows for permutation invariance, meaning the model can handle various orderings of agent inputs without performance degradation. This capability is vital for scenarios where agents may interact in numerous unpredictable ways.
2. Diffusion in Latent Space: To enhance the model's representation capacity and efficiency, the authors utilize Principal Component Analysis (PCA) to work in a compressed trajectory latent space. By capturing the primary modes of trajectory variance, the model reduces computational complexity while maintaining high fidelity predictions.
3. Controlled Sampling Framework: A significant extension of the diffusion model is the ability to perform constrained sampling using differentiable cost functions. This framework allows the model to incorporate additional priors or rules during inference, facilitating the creation of tailored scenarios and ensuring predictions adhere to physical and behavioral constraints.
4. Exact Log Probability Computation: The paper outlines a method to compute the exact log probability for generated samples. This capability allows for filtering and scoring the quality of trajectory predictions, ensuring that sampled trajectories align closely with learned distributions.

Empirical Validation

\ours{} is empirically validated on the Waymo Open Motion Dataset, achieving state-of-the-art performance in tasks requiring joint predictions for multiple interacting agents. Key metrics such as minSADE, minSFDE, and Overlap confirm the model's robustness in handling highly interactive scenarios, surpassing existing approaches like SceneTransformer and MultiPath++ in key quality metrics.

Implications and Future Directions

The paper's results illustrate the potential of diffusion models in the advancement of motion prediction systems for autonomous vehicles. By allowing for the modeling of complex joint distributions and enhancing controllability through constrained sampling, \ours{} provides a powerful tool for developing more reliable prediction systems in autonomous navigation. Future research could explore the extension of these techniques to other domains within AI, such as robotic planning and human-robot interaction, where predicting multiple-agent behaviors is crucial. Additionally, scaling these methodologies to encompass even larger datasets and more agents could further bolster their applicability and reliability in real-world applications.