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Unified Prediction of Pedestrian Intention to Jaywalk Based on Parallel Deep Learning Scheme
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.
Query Intent Detection for Medical Advice: Training Data Construction and Intent Classification
Tae-Hoon Lee, Young-Min Kim, Eunji Jeong, Seon-Ok Na
http://doi.org/10.5626/JOK.2021.48.8.878
In most task-oriented dialogue systems, intent detection and named entity recognition need to precede. This paper deals with the query intent detection to construct a dialogue system for medical advice. We start from the appropriate intent categories for the final goal. We also describe in detail the data collection, training data construction, and the guidelines for the manual annotation. BERT-based classification model has been used for query intent detection. KorBERT, a Korean version of BERT has been also tested for detection. To verify that the DNN-based models outperform the traditional machine learning methods even for a mid-sized dataset, we also tested SVM, which produces a good result in general for such dataset. The F1 scores of SVM, BERT, and KorBERT are 69%, 78%, and 84% respectively. For future work, we will try to increase intent detection performance through dataset improvement.
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