Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion

Abstract: Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states. Unlike existing stochastic trajectory prediction methods which usually use a latent variable to represent multi-modality, we explicitly simulate the process of human motion variation from indeterminate to determinate. In this paper, we present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID), in which we progressively discard indeterminacy from all the walkable areas until reaching the desired trajectory. This process is learned with a parameterized Markov chain conditioned by the observed trajectories. We can adjust the length of the chain to control the degree of indeterminacy and balance the diversity and determinacy of the predictions. Specifically, we encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Extensive experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method. Code is available at https://github.com/gutianpei/MID.

• The paper introduces MID, a framework that utilizes a reverse diffusion process to progressively refine trajectory predictions and reduce uncertainty.
• It employs a Transformer-based architecture with an adjustable Markov chain to effectively capture temporal dependencies and balance prediction accuracy with diversity.
• Experimental results on benchmark datasets demonstrate that MID outperforms traditional models, particularly in complex multi-agent interaction scenarios.

Overview of "Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion"

This paper introduces a novel framework for stochastic trajectory prediction, which considers the inherent indeterminacy present in human behavior. The framework, termed Motion Indeterminacy Diffusion (MID), leverages a reverse diffusion process to progressively reduce uncertainty in trajectory predictions, contrasting with traditionally employed latent variable models that represent multi-modality in human motion.

Methodology

The crux of MID is to model trajectory prediction as a reverse diffusion process that gradually reduces indeterminacy from a noisy distribution representing all possible walkable areas to a specific trajectory. This is achieved through a parameterized Markov chain, conditioned by observed trajectories. The authors introduce several key components in this method:

1. Diffusion Process: The forward diffusion process corrupts the target trajectory into Gaussian noise, simulating the increase in indeterminacy. Conversely, the reverse diffusion process estimates the original trajectory by regressing iteratively back from noise, reducing indeterminacy.
2. Transformer-based Architecture: The architecture captures temporal dependencies in trajectories, contrasting with traditional Recurrent Neural Network (RNN) based architectures like LSTMs. This structure allows the model to harness complex temporal patterns in pedestrian movements effectively.
3. Adjustable Indeterminacy: The length of the Markov chain can be tailored to balance the diversity and accuracy of predictions, allowing adaptability to dynamic environments by modulating the level of indeterminacy captured in the process.

The MID framework is trained using variational inference to maximize the likelihood of the predicted trajectory, adopting a loss function that combines several components, ensuring both correspondence to data and efficient progression through the diffusion chain.

Experimental Results

The authors validate MID on two popular datasets: the Stanford Drone dataset and the ETH/UCY datasets, which are benchmarks for human trajectory prediction scenarios. The results reveal that MID achieves superior performance compared to existing methods, particularly in scenarios with complex multi-agent interactions.

• On the Stanford Drone dataset, MID achieves better performance in terms of ADE (Average Displacement Error) and FDE (Final Displacement Error) with a minimal number of samples, emphasizing its efficiency in generating high-quality trajectory predictions.
• On the ETH/UCY datasets, although performance is comparable to state-of-the-art methods, the ability to dynamically reduce predictive uncertainty is emphasized.

Implications and Future Directions

The introduction of Motion Indeterminacy Diffusion offers significant implications for trajectory prediction systems in robotics, autonomous driving, and interactive AI domains. By providing a mechanism to control the degree of predictive uncertainty, MID can be tailored for applications that require adaptive response to environmental dynamics and human interaction cues.

Furthermore, MID's architecture, leveraging Transformer networks, suggests additional avenues for exploration in modeling temporal dynamics in trajectory forecasting. Future research could expand on enhancing the efficiency of the diffusion process and integrating more contextual data, such as environmental cues or interaction models, to further refine trajectory prediction capabilities.

The main limitation noted is the computational expense of the reverse diffusion process, an area that invites optimization through reduced steps or more efficient sampling techniques. Integrating recent advancements in sampling efficiency with MID presents a promising direction.

In summary, this paper presents a compelling framework for trajectory prediction, placing emphasis on reducing predictive uncertainty and leveraging advanced neural architectures for temporal modeling, thus contributing a versatile and adaptive approach to trajectory forecasting challenges.