드론 영상 인식과 Transformer를 활용한 도심 주행 차량의 차로 변경 예측 모델 연구

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Lane changes pose a risk of accidents due to lane interference between vehicles, affecting traffic flow. Therefore, research on predicting lane changes is necessary to reduce accident risks and improve the traffic system. This study proposes a method for extracting vehicle information from top-view drone footage and predicting lane changes.

Author abstract

Lane changes pose a risk of accidents due to lane interference between vehicles, affecting traffic flow. Therefore, research on predicting lane changes is necessary to reduce accident risks and improve the traffic system. This study proposes a method for extracting vehicle information from top-view drone footage and predicting lane changes. We used the deep learning-based object detection model YOLOv8 for vehicle detection in the footage and extracted Traffic Context from the detected objects. Using this extracted information, we performed lane change prediction utilizing a Transformer model. For our experiments, we utilized drone footage of road conditions in Guseong-dong, Yuseong-gu, Daejeon. The experimental results demonstrated that YOLOv8 achieved a high object detection performance with an mAP50 of 0.99 and an mAP50-95 of 0.75, indicating a high degree of agreement between detected and actual objects. In terms of lane change prediction, the Transformer m odel outperformed other models, achieving an accuracy of 0.977, which is 0.02 higher, and the lowest misclassification rate in the confusion matrix. This study is expected to contribute to improving traffic systems by serving as a valuable resource for efficient traffic flow management in the future.

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Citation-ready BibTeX

@article{noh2024transformer,
  title   = {드론 영상 인식과 Transformer를 활용한 도심 주행 차량의 차로 변경 예측 모델 연구},
  author  = {홍석준 and 임재균 and 이호우 and 가동호 and 이창희 and 노병준},
  year    = {2024},
  journal = {대한교통학회논문지 42(5), pp.551-564, Oct 2024.}
}

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