模拟激光雷达点云在路侧感知算法中的应用

路侧感知算法融合车载感知算法实现了超视距感知, 基于深度学习的感知算法性能取决于激光雷达点云标签标注的质量, 而点云标签相对于二维图像更难标注, 需要大量时间人力成本进行标注, 且现行感知算法都是针对于车载激光雷达. 针对这些问题, 本文提出了一种基于路侧激光雷达栅格特征聚类的感知算法, 该算法首先对路侧激光雷达点云栅格化并提取特征, 再构建深度学习方法模型学习栅格的初级感知信息, 最后根据初级感知信息进行聚类完成感知检测. 本文还利用仿真平台模拟路侧激光雷达点云, 并研究混合数据集在感知算法训练上的应用, 基于模拟数据预训模型微调(Fine-tune)在感知算法上的应用. 实验结果表明, 本文提出的路侧感知算法具有较高的实时性与可靠性, 模拟路侧激光雷达点云有助于路侧感知算法训练, 减少路侧感知算法对标注工作的依赖, 提高感知算法性能.

Application of Simulated LiDAR Point Cloud in Roadside Perception Algorithm

The roadside perception algorithm is integrated with the on-board perception algorithm to achieve over-the-horizon perception. The performance of the perception algorithm based on deep learning depends on the quality of the point cloud annotation of lidar which is harder than the annotation of 2D images because it takes longer time and calls for much manpower. In addition, existing perception algorithms based mainly on the on-board lidar. In this study, we proposes a perception algorithm based on the feature clustering of roadside lidar grids. This algorithm rasterizes the point cloud of roadside lidar and extract the features, then learn the primary perception information of the grids by creating a deep learning model for clustering on this basis. We also simulate the point cloud of roadside lidar via a simulation platform, and studies the application of the hybrid data set in training perception algorithm, which is fine-tuned by the pre-training model of simulation data. Experimental results show that the proposed perception algorithm is reliable with real-time service. Besides, simulating the point cloud of roadside lidar helps with the training of this algorithm and reduces its dependence on annotation, improving its performance.