DiffuTraj: A Stochastic Vessel Trajectory Prediction Approach via Guided Diffusion Process

TL;DR

DiffuTraj introduces a novel vessel trajectory prediction framework using guided diffusion processes, explicitly modeling uncertainty reduction to generate accurate, environment-aware maritime trajectories, outperforming existing stochastic methods.

Contribution

The paper presents a new diffusion-based approach for vessel trajectory prediction that explicitly models the transition from uncertainty to certainty, incorporating environmental context and vessel interactions.

Findings

Outperforms existing stochastic trajectory prediction methods.

Effectively models multi-modality and dynamic maritime behaviors.

Demonstrates superior accuracy on benchmark datasets.

Abstract

Maritime vessel maneuvers, characterized by their inherent complexity and indeterminacy, requires vessel trajectory prediction system capable of modeling the multi-modality nature of future motion states. Conventional stochastic trajectory prediction methods utilize latent variables to represent the multi-modality of vessel motion, however, tends to overlook the complexity and dynamics inherent in maritime behavior. In contrast, we explicitly simulate the transition of vessel motion from uncertainty towards a state of certainty, effectively handling future indeterminacy in dynamic scenes. In this paper, we present a novel framework (\textit{DiffuTraj}) to conceptualize the trajectory prediction task as a guided reverse process of motion pattern uncertainty diffusion, in which we progressively remove uncertainty from maritime regions to delineate the intended trajectory. Specifically, we encode the previous states of the target vessel, vessel-vessel interactions, and the environment context as guiding factors for trajectory generation. Subsequently, we devise a transformer-based conditional denoiser to capture spatio-temporal dependencies, enabling the generation of trajectories better aligned for particular maritime environment. Comprehensive experiments on vessel trajectory prediction benchmarks demonstrate the superiority of our method.