基于激光雷达数据的行人检测

在自动驾驶领域涉及的众多任务中，行人识别是必不可少的技术之一。针对基于图像数据的行人检测算法无法获得行人深度的问题，提出了基于激光雷达数据的行人检测算法。该算法结合传统基于激光雷达数据的运动目标识别算法和基于深度学习的点云识别算法，可以在不依赖图像数据的条件下感知和检测行人，进而获取行人的准确三维位置，辅助自动驾驶控制系统作出合理决策。该算法在KITTI三维目标检测任务数据集上进行性能测试，中等难度测试达到33.37%的平均准确度，其表现领先于其他基于激光雷达的算法，充分证明了该方法的有效性。

Pedestrian detection based on LiDAR data

Pedestrian detection is a task which is necessary among all tasks leveraged in automatic driving domain. Traditional pedestrian detection algorithms took fully advantage of image data, unable to obtain depth of objects. To address aforementioned issue, this paper proposed a method based on raw LiDAR point cloud data. The proposed method combined traditional moving object detection in LiDAR data and point cloud recognition by deep learning, and was capable of perception and pedestrian detection without images, obtaining 3D location of pedestrian, therefore helping central control system make reasonable decisions. This method experimented in 3D object detection task of KITTI dataset, obtained 33.37% AP(average precision) on moderate cases. The results show that the proposed method performs better than other algorithm based on LiDAR data, which hence indicates the effectiveness of proposed method.