PURPOSES : This study aimed to compare the object detection performance based on various analysis methods using point-cloud data collected from LiDAR sensors with the goal of contributing to safer road environments. The findings of this study provide essential information that enables automated vehicles to accurately perceive their surroundings and effectively avoid potential hazards. Furthermore, they serve as a foundation for LiDAR sensor application to traffic monitoring, thereby enabling the collection and analysis of real-time traffic data in road environments. METHODS : Object detection was performed using models based on different point-cloud processing methods using the KITTI dataset, which consists of real-world driving environment data. The models included PointPillars for the voxel-based approach, PartA2-Net for the point-based approach, and PV-RCNN for the point+voxel-based approach. The performance of each model was compared using the mean average precision (mAP) metric. RESULTS : While all models exhibited a strong performance, PV-RCNN achieved the highest performance across easy, moderate, and hard difficulty levels. PV-RCNN outperformed the other models in bounding box (Bbox), bird’s eye view (BEV), and 3D object detection tasks. These results highlight PV-RCNN's ability to maintain a high performance across diverse driving environments by combining the efficiency of the voxel-based method with the precision of the point-based method. These findings provide foundational insights not only for automated vehicles but also for traffic detection, enabling the accurate detection of various objects in complex road environments. In urban settings, models such as PV-RCNN may be more suitable, whereas in situations requiring real-time processing efficiency, the voxelbased PointPillars model could be advantageous. These findings offer important insights into the model that is best suited for specific scenarios. CONCLUSIONS : The findings of this study aid enhance the safety and reliability of automated driving systems by enabling vehicles to perceive their surroundings accurately and avoid potential hazards at an early stage. Furthermore, the use of LiDAR sensors for traffic monitoring is expected to optimize traffic flow by collecting and analyzing real-time traffic data from road environments.

선박은 충돌방지를 위해 해상충돌예방규칙에 의해 운항한다. 하지만 다수의 선박이 동시에 운항하는 특수상황 시에는 해상충 돌예방규칙을 적용하기 곤란하며 이때는 운항자의 개인능력에 의한다. 이러한 경우 해상교통관제를 통한 교통상황 관리가 필요하다. 이에 전 세계적으로 VTS(Vessel Traffic Services)를 통해 해상교통이 관리되고 있으며 운용 방법은 관제요원이 VTS 시스템을 이용하여 위험상황을 판단하고 통신시설을 이용하여 선박들에게 안전운항을 권고한다. 이 연구에서는 기존 방법에 AI(Artificial Intelligence) 기법을 추가하여 운항자의 관점에서 위험상황을 판단하는 방법에 대해 고찰한다. 또한, 관제 효율성 증대를 위해 AR(Augmented Reality)기법을 추가한 해상교통안전모니터링 시스템에 대해 설명한다. 이 시스템은 위험상황 및 위험 우선순위 예측이 정량적으로 가능하여 복잡한 교통상황시 실제 운항자가 충돌회피하는 방법과 동일한 순차적 위험상황 해소가 가능하다. 특히, 위험상황을 관제요원의 관점뿐만 아니라 각 선박의 운항자의 관점에서 분석할 수 있어 기존의 방법보다 실제적이다. 또한, 분석결과를 통해 정량적인 위험수역 파악이 가능하여 충돌회피를 위한 권고항로 지원이 가능하다. 결과적으로 이 시스템은 해상교통상황이 복잡한 해역에서의 선박간 충돌방지에 도움이 될 것이다. 특히, 해양분야 제4차 산업혁명에 주요한 분야를 차지하는 자율운항선박에 충돌방지 기능으로 사용될 수 있을 것이다.

PURPOSES: Traffic cameras have been installed to reduce traffic accidents. The effectiveness of traffic cameras has been proved by dozens of studies, but recently questions over its effectiveness have been raised by a series of studies. In this study, the effectiveness of traffic cameras was analyzed with a focus on different road environments. METHODS : The effectiveness of the traffic cameras was analyzed by extracting the occurence frequency before and after camera installation. The effect of reduction was analyzed comprehensively considering the installation position, monitoring direction, and surrounding environment of traffic cameras. RESULTS : The result of this study is as follows. First, the installation of cameras in an area with relatively low accidental traffic was more effective. Secondly, the effect of camera installation on car-to-pedestrian collisions was better than that of car-to-car collisions. Thirdly, accidents tended to occur more frequently when cameras were installed in front of the accident-prone owing to the negative spill-over effect. CONCLUSIONS: The result can be used to guide placement of traffic cameras. Moreover, the installation of cameras with consideration of the road environment is expected to contribute to a reduction in traffic fatalities.