Leapfrog Diffusion Model for Stochastic Trajectory Prediction (2303.10895v1)

Abstract: To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have revealed their tremendous representation capacities in numerous generation tasks, showing potential for stochastic trajectory prediction. However, expensive time consumption prevents diffusion models from real-time prediction, since a large number of denoising steps are required to assure sufficient representation ability. To resolve the dilemma, we present LEapfrog Diffusion model (LED), a novel diffusion-based trajectory prediction model, which provides real-time, precise, and diverse predictions. The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed. Moreover, the leapfrog initializer is trained to appropriately allocate correlated samples to provide a diversity of predicted future trajectories, significantly improving prediction performances. Extensive experiments on four real-world datasets, including NBA/NFL/SDD/ETH-UCY, show that LED consistently improves performance and achieves 23.7%/21.9% ADE/FDE improvement on NFL. The proposed LED also speeds up the inference 19.3/30.8/24.3/25.1 times compared to the standard diffusion model on NBA/NFL/SDD/ETH-UCY, satisfying real-time inference needs. Code is available at https://github.com/MediaBrain-SJTU/LED.

• The paper introduces the LED, which uses a leapfrog initializer to reduce denoising steps while capturing expressive multimodal trajectory distributions.
• It achieves significant accuracy gains with a 23.7%/21.9% improvement in ADE/FDE on NFL data through a combination of mean-variance estimation and trainable reparameterization.
• Experiments on four datasets demonstrate LED’s robustness and a 30-fold speed-up in inference, making it highly suitable for real-time applications.

An Evaluation of the Leapfrog Diffusion Model for Stochastic Trajectory Prediction

The ability to effectively model stochastic trajectory prediction has significant implications across various domains, such as autonomous driving, drone navigation, surveillance systems, and human-robot interaction. The paper "Leapfrog Diffusion Model for Stochastic Trajectory Prediction" proposes a novel approach utilizing diffusion models to address the challenges associated with prediction accuracy and real-time inference speed, presenting the Leapfrog Diffusion Model (LED) as a substantial advancement in this space.

Core Contributions

The paper introduces the LED, a diffusion-based model designed to enhance both performance and efficiency in trajectory prediction tasks. Its fundamental innovation lies in the leapfrog initializer, which aims to leapfrog multiple denoising steps common to traditional diffusion models. This approach directly estimates an expressive multi-modal distribution of possible trajectories, which has the potential to provide accurate predictions with significantly reduced computational cost.

The LED framework leverages a sophisticated combination of mean, variance, and sample predictions to capture the intricacies of the trajectory distribution. By incorporating a trainable reparameterization technique, the model achieves more stable training, while also accelerating inference by effectively capturing and utilizing past trajectories through fast sampling strategies. This presents a marked improvement over conventional methods which typically require a large number of denoising steps, often resulting in prohibitive time consumption unsuitable for real-time applications.

Key Findings and Results

The paper substantiates its claims through extensive experimentation across four datasets, notably NBA, NFL, SDD, and ETH-UCY. It demonstrates that the LED consistently delivers superior accuracy in trajectory prediction, achieving a 23.7%/21.9% improvement in ADE/FDE on the NFL dataset over previous state-of-the-art models. These empirical results suggest that the LED is not only effective but also robust across different stochastic prediction tasks. Furthermore, LED achieves up to 30 times speed-up in inference compared to standard diffusion models, effectively aligning with the real-time processing requirements often essential in practical implementations.

Implications and Future Directions

The implications of this research stretch beyond the specific problem of trajectory prediction, touching upon broader questions in the design and implementation of efficient generative models for time-series analysis. By reducing the computational overhead without sacrificing prediction quality, this work suggests a direction forward for designing generative models that are both powerful and practical.

While the LED model offers considerable advantages, its efficacy is tested on trajectory datasets, which are lower-dimensional when contrasted with image or video data. Future explorations could extend these methodologies to higher-dimensional generative tasks, investigating whether the improvements in speed and representation capacity demonstrated by LED can be generalized.

Additionally, the paper acknowledges potential improvements such as explicit supervision for the model's leapfrog steps. Such refinements may yield even greater accuracy and reliability in modeling complex stochastic systems, expanding the applicability of this model to a broader array of dynamic prediction scenarios.

In conclusion, this paper delivers a technically rigorous examination of a novel diffusion-based model that holds substantial promise for improving stochastic trajectory prediction, especially in scenarios where real-time inference is paramount. The LED's balance of precision and efficiency establishes a strong foundation for ongoing advancements in this sphere.