Unified Prediction of Pedestrian Intention to Jaywalk Based on Parallel Deep Learning Scheme

Sikyung Kim, Young-Min Kim

http://doi.org/10.5626/JOK.2024.51.6.545

Urbanization has led to diversification in traffic accidents and parking issues, with pedestrian accidents at crosswalks accounting for over 30% of traffic fatalities. Particularly concerning are situations where pedestrians are not anticipated by drivers during red signal conditions, as the potential for severe injuries is high. To address this issue, we propose a deep learning-based integrated pedestrian crossing intent prediction system. The system uses the YOLOv5 object detection model to identify pedestrian actions that indicate crossing intent. At the same time, it utilzes the MMPOSE joint prediction model to classify the pedestrian's perspective. By analyzing pedestrian actions, perspectives, and the distance between the pedestrian and the crosswalk, the system predicts crossing intent in various scenarios. Future research based on this study is expected to contribute to diverse application studies aimed at enhancing traffic safety in autonomous driving.