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.

PURPOSES: The density in uninterrupted traffic flow facilities plays an important role in representing the current status of traffic flow. For example, the density is used for the primary measures of effectiveness in the capacity analysis for freeway facilities. Therefore, the estimation of density has been a long and tough task for traffic engineers for a long time. This study was initiated to evaluate the performance of density values that were estimated using VDS data and two traditional methods, including a method using traffic flow theory and another method using occupancy by comparing the density values estimated using vehicular trajectory data generated from a radar detector. METHODS: In this study, a radar detector which can generate very accurate vehicular trajectory within the range of 250 m on the Joongbu expressway near to Dongseoul tollgate, where two VDS were already installed. The first task was to estimate densities using different data and methods. Thus, the density values were estimated using two traditional methods and the VDS data on the Joongbu expressway. The density values were compared with those estimated using the vehicular trajectory data in order to evaluate the quality of density estimation. Then, the relationship between the space mean speed and density were drawn using two sets of densities and speeds based on the VDS data and one set of those using the radar detector data. CONCLUSIONS: As a result, the three sets of density showed minor differences when the density values were under 20 vehicles per km per lane. However, as the density values become greater than 20 vehicles per km per lane, the three methods showed a significant difference among on another. The density using the vehicular trajectory data showed the lowest values in general. Based on the in-depth study, it was found out that the space mean speed plays a critical role in the calculation of density. The speed estimated from the VDS data was higher than that from the radar detector. In order to validate the difference in the speed data, the traffic flow models using the relationships between the space mean speed and the density were carefully examined in this study. Conclusively, the traffic flow models generated using the radar data seems to be more realistic.

본 연구에서는 차량검지기의 속도측정 성능평가방법을 개발하였다. 개발된 성능평가방법에서는 오차요인들을 기준속도에 반영하며 측정불확도의 개념을 적용하였다. 기존연구, 통계적 처리기법, 기존교통단속장비 및 차량검지시스템의 속도측정 성능평가방법 등에 대한 고찰을 통해 기존평가방법의 문제점을 도출하고 개선된 성능평가방법을 개발하였다, 실제 현장에 설치된 차량검지기에 대해서 기존평가방법과 개발방법을 적용해본 결과 기존평가방법은 평가기준에 적합하나 개발방법은 평가기준을 만족시키지 못하고 있다. 이러한 결과는 기존성능평가방법이 측정 시의 오차요인들을 충분히 고려하지 못해서 평가대상장비의 성능을 고평가할 가능성이 있음을 의미하며, 반면에 개발모형은 측정 시의 변동요인인 오차를 고려하므로 기존평가방법 보다 정확함을 나타낸다.

고속도로 합류부 지점의 감응식 루프 검지기를 통한 정보 수집의 질은 검지기의 설치 위치와 관련이 있다. 검지기로부터 얻은 교풍자료들은 안정된 교통의 흐름과 높은 안전 수준을 유지하게 위해 필수적으로 사용된다. 또한, 이러한 정보는 교통관리전략 위한 입력 자료로도 사용된다. 본 연구에서는 대표적인 교통관리 전략중 하나인 램프 미터링의 효과를 극대화하기 위해 고속도로 합류부에서의 검지기 최적 설치 위치를 통계기법을 이용하여 산정하였고, 주요한 분석도구로써 미시적 교통 시뮬레이션 모형인 PARAMICS를 사용하였다. 검지기 설치 위치 산정은 통계분석을 통해 도로 구간 별 교통류 특성에 매우 의존하고 있음을 규명하였다.