Lidar based on the optical phased array (OPA) and frequency-modulated continuous wave (FMCW) technology stands out in automotive applications due to its all-solid-state design, high reliability, and remarkable resistance to interference. However, while FMCW coherent detection enhances the interference resistance capabilities, it concurrently results in a significant increase in depth computation, becoming a primary constraint for improving point cloud density in such perception systems. To address this challenge, this study introduces a lidar solution leveraging the flexible scanning characteristics of OPA. The proposed system categorizes target types within the scene based on RGB images. Subsequently, it performs scans with varying angular resolutions depending on the importance of the targets. Experimental results demonstrate that, compared to traditional scanning methods, the target-adaptive method based on semantic segmentation reduces the number of points to about one-quarter while maintaining the resolution of the primary target area. Conversely, with a similar number of points, the proposed approach increases the point cloud density of the primary target area by about four times.

中文翻译：

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目标自适应光学相控阵激光雷达

基于光学相控阵（OPA）和调频连续波（FMCW）技术的激光雷达因其全固态设计、高可靠性和出色的抗干扰能力而在汽车应用中脱颖而出。然而，FMCW相干检测在增强抗干扰能力的同时，也导致深度计算量显着增加，成为提高此类感知系统点云密度的主要制约因素。为了应对这一挑战，本研究引入了一种利用 OPA 灵活扫描特性的激光雷达解决方案。所提出的系统根据 RGB 图像对场景内的目标类型进行分类。随后，它根据目标的重要性以不同的角分辨率执行扫描。实验结果表明，与传统扫描方法相比，基于语义分割的目标自适应方法在保持主要目标区域分辨率的同时，将点数减少到四分之一左右。相反，在点数相似的情况下，所提出的方法将主要目标区域的点云密度增加了约四倍。