ITSC 2025 Paper Abstract

Paper TH-EA-T30.4

Liu, Yu (City University of Hong Kong, Hong Kong SAR, China and Southern ), Liu, Zhijie (Southern University of Science and Technology), Ren, Xiao (Southern University of Science and Technology), Li, You-Fu (City University of Hong Kong, Hong Kong SAR, China), Kong, He (Southern University of Science and Technology, Shenzhen, China)

Intention Enhanced Diffusion Model for Multimodal Pedestrian Trajectory Prediction

Scheduled for presentation during the Regular Session "S30b-Intelligent Modeling and Prediction of Traffic Dynamics" (TH-EA-T30), Thursday, November 20, 2025, 14:30−14:50, Gold Coast

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords AI, Machine Learning for Real-time Traffic Flow Prediction and Management, AI, Machine Learning and Predictive Analytics for Traffic Incident Detection and Management

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.