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首页> 外文期刊>IEEE Signal Processing Magazine >Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems
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Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems

机译：自主驾驶的激光乐队：汽车激光器和感知系统的原则，挑战和趋势

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Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians, and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of lidar systems to complement camera- or radar-based perception systems. This article presents a review of state-of-the-art automotive lidar technologies and the perception algorithms used with those technologies. Lidar systems are introduced first by analyzing such a system?s main components, from laser transmitter to beamscanning mechanism. The advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for lidar data processing is detailed from an autonomous vehicle perspective. The model-driven approaches and emerging deep learning (DL) solutions are reviewed. Finally, we provide an overview of the limitations, challenges, and trends for automotive lidars and perception systems.
机译：自动车辆依赖他们的感知系统来获取有关他们周围环境的信息。有必要检测其他车辆，行人和其他相关实体的存在。安全问题和对准确估计的需求导致利雷达系统引入了补充相机或基于雷达的感知系统。本文提出了述评最先进的汽车激光雷达技术和与这些技术一起使用的感知算法。首先通过分析这种系统的主要部件，从激光发射器到波束扫描机构来首先介绍LIDAR系统。引入并比较了各种解决方案的优点/缺点和当前状态。然后，从自主车辆透视中详细说明了LIDAR数据处理的特定感知管道。综述了模型驱动的方法和新兴深度学习（DL）解决方案。最后，我们概述了汽车Lidars和感知系统的局限性，挑战和趋势。

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