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.

본 논문에서는 3D 그래픽에서 빠르고 정확한 충돌검사(collision-detection)는 3D공간에서 표준객체를 중심으로 하는 연구가 많이 이루어져 왔다. 3D그래픽 분야에서 H/W의 놀라운 발달과 다양한 3D그래픽 관련 논문에서 3D객체의 충돌 속도의 성능 향상뿐만 아니라 사실적인 표현에 깊은 관심을 가지고 있다. 3D 그래픽 알고리즘 중에서 표준 3D 객체의 다양한 충돌 알고리즘을 특징을 분석하고, 기존의 3D 객체의 단순한 계층 구조에서 LOD(Level-of-Detail)를 이용한 알고리즘를 제안한다. 이 알고리즘을 이용하여 3D공간상에서 LOD(Level-of-Detail) 알고리즘을 적용시켜서, LOD단계가 높은 (가까운) 곳에서는 객체의 유향상자를 자세히 검사하고, LOD단계가 낮은(먼곳)에 위치한 객체의 유향상자는 간략히 검사를 적용3D객체가 3D 공간상에서 충돌검사의 성능을 향상시키고 3D 그래픽에서 중요한 요소인 3차원 공간상의 효율적인 렌더링과 사실적인 표현을 제안하여 실시간을 중요시 하는 3D 게임에서 사실감과 효율성을 높였다.