Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction

• URL: http://arxiv.org/abs/2508.07146v1
• Date: Sun, 10 Aug 2025 02:36:33 GMT
• Title: Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction
• Authors: Yu Liu, Zhijie Liu, Xiao Ren, You-Fu Li, He Kong,
• Abstract summary: We propose a diffusion-based pedestrian trajectory prediction framework that incorporates both short-term and long-term motion intentions.<n>The proposed framework is evaluated on the widely used ETH, UCY, and SDD benchmarks, demonstrating competitive results against state-of-the-art methods.
• Score: 15.151965172049271
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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