Work

OPTP-System: A Lightweight Pedestrian Trajectory Prediction System for Complex Occlusion Environments

Year: 2026

Type: article

Abstract: Pedestrian trajectory prediction in complex occlusion environments remains a critical challenge for autonomous driving systems. Although high-precision prediction models have achieved notable success, they often entail substantial computational overhead and struggle to maintain both accuracy and physical plausibility under real-world occluded conditions. To address these limitations, this paper proposes OPTP-System, a lightweight prediction framework that integrates YOLOv11 with DeepSORT for robust multi-pedestrian tracking in occluded scenes. An extended Kalman filter (EKF)-based motion prediction module is employed to generate trajectory forecasts, while the EKF-derived prior knowledge guides detection re-searching in occluded regions. Furthermore, feedback from trajectory smoothing refines detection confidence, substantially enhancing the model’s capability for continuous tracking and prediction under severe occlusion. Experimental results under challenging occlusion settings (exceeding 50% occlusion) show that the proposed model reduces ADE and FDE by 30.0% and 29.3%, respectively, compared to state-of-the-art methods. These findings demonstrate that OPTP-System achieves superior prediction accuracy while maintaining computational efficiency, offering a practical solution for reliable pedestrian trajectory prediction in complex traffic environments.

Source: Electronics

DOI: 10.3390/electronics15112247

Authors: Zijian Lin, Hong Huang, Yirui Zhang, Wenfeng Zhao

Institution: South China Agricultural University

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896