The 3D object detection capabilities in urban environments have been
enormously improved by recent developments in Light Detection and Range (LiDAR)
technology. This paper presents a novel framework that transforms the detection
and analysis of 3D objects in traffic scenarios by utilizing the power of
elevated LiDAR sensors. We are presenting our methodology's remarkable capacity
to collect complex 3D point cloud data, which allows us to accurately and in
detail capture the dynamics of urban traffic. Due to the limitation in
obtaining real-world traffic datasets, we utilize the simulator to generate 3D
point cloud for specific scenarios. To support our experimental analysis, we
firstly simulate various 3D point cloud traffic-related objects. Then, we use
this dataset as a basis for training and evaluating our 3D object detection
models, in identifying and monitoring both vehicles and pedestrians in
simulated urban traffic environments. Next, we fine tune the Point
Voxel-Region-based Convolutional Neural Network (PV-RCNN) architecture, making
it more suited to handle and understand the massive volumes of point cloud data
generated by our urban traffic simulations. Our results show the effectiveness
of the proposed solution in accurately detecting objects in traffic scenes and
highlight the role of LiDAR in improving urban safety and advancing intelligent
transportation systems.

本论文介绍了一种利用提升式激光雷达（LiDAR）传感器的能力，通过转变交通场景中的三维物体检测和分析的框架来改善在城市环境中的三维物体检测能力。利用模拟器生成三维点云数据集，通过训练和评估三维物体检测模型，准确识别和监测模拟城市交通环境中的车辆和行人。针对由模拟交通生成的大量点云数据，优化了基于点体素区域的卷积神经网络（PV-RCNN）架构，提升了其处理和理解能力。实验结果表明所提出的解决方案在准确检测交通场景中的物体方面的有效性，并强调了激光雷达对提升城市安全和推动智能交通系统的作用。