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.

中文翻译：

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自动驾驶激光雷达：汽车激光雷达和感知系统的原理、挑战和趋势

自动驾驶汽车依靠其感知系统来获取有关其周围环境的信息。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引入了激光雷达系统，以补充基于摄像头或雷达的感知系统。本文回顾了最先进的汽车激光雷达技术以及与这些技术一起使用的感知算法。首先通过分析此类系统的主要组件（从激光发射器到光束扫描机制）来介绍激光雷达系统。介绍和比较了各种解决方案的优缺点和现状。然后，从自动驾驶汽车的角度详细介绍了激光雷达数据处理的具体感知管道。审查了模型驱动的方法和新兴的深度学习 (DL) 解决方案。最后，我们概述了汽车激光雷达和感知系统的局限性、挑战和趋势。