Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction

TL;DR

This paper introduces a diffusion-based pedestrian trajectory prediction framework that explicitly models both short-term and long-term intentions, improving accuracy and capturing multimodal behaviors for autonomous vehicle applications.

Contribution

It presents a novel diffusion model incorporating semantic intent modeling via residual polar representation and token-based goal prediction, enhancing trajectory prediction accuracy.

Findings

Achieves competitive results on ETH, UCY, and SDD benchmarks.

Effectively models multimodal and context-aware pedestrian intentions.

Improves prediction accuracy over existing diffusion-based methods.

Abstract

Predicting pedestrian motion trajectories is critical for the path planning and motion control of autonomous vehicles. Recent diffusion-based models have shown promising results in capturing the inherent stochasticity of pedestrian behavior for trajectory prediction. However, the absence of explicit semantic modelling of pedestrian intent in many diffusion-based methods may result in misinterpreted behaviors and reduced prediction accuracy. To address the above challenges, we propose a diffusion-based pedestrian trajectory prediction framework that incorporates both short-term and long-term motion intentions. Short-term intent is modelled using a residual polar representation, which decouples direction and magnitude to capture fine-grained local motion patterns. Long-term intent is estimated through a learnable, token-based endpoint predictor that generates multiple candidate goals with associated probabilities, enabling multimodal and context-aware intention modelling. Furthermore, we enhance the diffusion process by incorporating adaptive guidance and a residual noise predictor that dynamically refines denoising accuracy. The proposed framework is evaluated on the widely used ETH, UCY, and SDD benchmarks, demonstrating competitive results against state-of-the-art methods.