Autonomous Vehicles (AVs) being developed these days rely on various sensor technologies to sense and perceive the world around them. The sensor outputs are subsequently used by the Automated Driving System (ADS) onboard the vehicle to make decisions that affect its trajectory and how it interacts with the physical world. The main sensor technologies being utilized for sensing and perception (S&P) are LiDAR (Light Detection and Ranging), camera, RADAR (Radio Detection and Ranging), and ultrasound. Different environmental parameters would have different effects on the performance of each sensor, thereby affecting the S&P and decision-making (DM) of an AV. In this publication, we explore the effects of different environmental parameters on LiDARs and cameras, leading us to conduct a study to better understand the impact of several of these parameters on LiDAR performance. From the experiments undertaken, the goal is to identify some of the weaknesses and challenges that a LiDAR may face when an AV is using it. This informs AV regulators in Singapore of the effects of different environmental parameters on AV sensors so that they can determine testing standards and specifications which will assess the adequacy of LiDAR systems installed for local AV operations more robustly. Our approach adopts the LiDAR test methodology first developed in the Urban Mobility Grand Challenge (UMGC-L010) White Paper on LiDAR performance against selected Automotive Paints.

翻译：近年来开发的自动驾驶车辆（AV）依赖多种传感器技术来感知和理解周围环境。传感器的输出随后由车辆搭载的自动驾驶系统（ADS）用于决策，这些决策会影响其行驶轨迹及与物理世界的交互方式。当前用于感知（S&P）的主要传感器技术包括激光雷达（LiDAR）、摄像头、雷达（RADAR）和超声波传感器。不同环境参数会对每种传感器的性能产生不同影响，进而影响AV的感知与决策（DM）。在本研究中，我们探讨了不同环境参数对激光雷达和摄像头的影响，并据此开展实验，以更深入地理解其中若干参数对激光雷达性能的作用。通过实验，我们旨在识别激光雷达在AV使用过程中可能面临的弱点与挑战。这为新加坡的AV监管机构提供了关于不同环境参数对AV传感器影响的信息，有助于其制定测试标准与规范，从而更稳健地评估本地AV运营中安装的激光雷达系统的充分性。我们的方法借鉴了《城市出行大挑战（UMGC-L010）》白皮书中首次提出的激光雷达针对选定汽车涂料的性能测试方法。