Intention Enhanced Diffusion Model for Multimodal Pedestrian Trajectory Prediction

TL;DR

This paper introduces a diffusion-based multimodal pedestrian trajectory prediction model that incorporates motion intentions for improved accuracy and interpretability, evaluated on ETH and UCY benchmarks.

Contribution

It explicitly models pedestrian intentions within a diffusion framework, enhancing prediction interpretability and performance over existing methods.

Findings

Achieves competitive results on ETH and UCY datasets.

Effectively captures pedestrian motion intentions.

Provides more interpretable trajectory predictions.

Abstract

Predicting pedestrian motion trajectories is critical for path planning and motion control of autonomous vehicles. However, accurately forecasting crowd trajectories remains a challenging task due to the inherently multimodal and uncertain nature of human motion. Recent diffusion-based models have shown promising results in capturing the stochasticity of pedestrian behavior for trajectory prediction. However, few diffusion-based approaches explicitly incorporate the underlying motion intentions of pedestrians, which can limit the interpretability and precision of prediction models. In this work, we propose a diffusion-based multimodal trajectory prediction model that incorporates pedestrians' motion intentions into the prediction framework. The motion intentions are decomposed into lateral and longitudinal components, and a pedestrian intention recognition module is introduced to enable the model to effectively capture these intentions. Furthermore, we adopt an efficient guidance mechanism that facilitates the generation of interpretable trajectories. The proposed framework is evaluated on two widely used human trajectory prediction benchmarks, ETH and UCY, on which it is compared against state-of-the-art methods. The experimental results demonstrate that our method achieves competitive performance.