컴퓨터 비전 기반의 횡단보도 내 차량-보행자 간 잠재적 충돌 위험 인식 방법 및 분석에 관한 연구

AI-ready brief

Most of the traffic accidents involving vulnerable pedestrians occur on crosswalks. To reduce the collision risk between vehicles and pedestrians, we need to identify the risky situations and find the factors affecting it. We propose a method identifying the potential collision risks of vehicle-pedestrian interactions near crosswalks by applying computer vision and artificial intelligence techniques on videos.

Author abstract

Most of the traffic accidents involving vulnerable pedestrians occur on crosswalks. To reduce the collision risk between vehicles and pedestrians, we need to identify the risky situations and find the factors affecting it. We propose a method identifying the potential collision risks of vehicle-pedestrian interactions near crosswalks by applying computer vision and artificial intelligence techniques on videos. First, we employ a mask R-CNN model and transformation matrix in OpenCV to recognize the precise positions of the road users (e.g. vehicle and pedestrian) in orthogonal view. Next, we track their trajectories using the modified Kalman filter method. After extracting their interactions from trajectories of road users, we analyze their potential collision risks near crosswalks. In our experiments, we collected the video footage from 9 cameras in Osan City, tracked road users in videos, and analyzed the potential collision risks between them. On this video, the accuracy of road user detection was about 90% for both the vehicles and pedestrians when the distance tolerance is 50cm. For the tracking accuracy, our modified Kalman filter outperformed the threshold and minimum distance method by 3%. In analysis part, we elicited the valuable information that the vehicle speed is high on a wide road without a speed camera and the pedestrian safety margin is small on unsignalized crosswalks. We further observed that around 50% of cars do not stop before crosswalk when pedestrians cross the un-signalized crosswalk without having special warning signs such as red urethane road pavement and school zone marking on road.

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Citation-ready BibTeX

@article{noh20236nlrxr,
  title   = {컴퓨터 비전 기반의 횡단보도 내 차량-보행자 간 잠재적 충돌 위험 인식 방법 및 분석에 관한 연구},
  author  = {노병준 and 조해찬 and 이혜진 and 가동호 and 여화수},
  year    = {2023},
  journal = {대한교통학회 41(1), pp.1-18, Feb 2023.}
}

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