无人驾驶系统中基于学习的相机和激光雷达模拟方法综述

Abstract—Perception sensors, particularly camera and Lidar,

are key elements of Autonomous Driving Systems (ADS) that

enable them to comprehend their surroundings for informed

driving and control decisions. Therefore, developing realistic

camera and Lidar simulation methods, also known as camera

and Lidar models, is of paramount importance to effectively

conduct simulation-based testing for ADS. Moreover, the rise

of deep learning-based perception models has propelled the

prevalence of perception sensor models as valuable tools for

synthesising diverse training datasets. The traditional sensor

simulation methods rely on computationally expensive physicsbased algorithms, specifically in complex systems such as ADS.

Hence, the current potential resides in learning-based models,

driven by the success of deep generative models in synthesising

high-dimensional data. This paper reviews the current state-ofthe-art in learning-based sensor simulation methods and validation approaches, focusing on two main types of perception

sensors: cameras and Lidars. This review covers two categories

of learning-based approaches, namely raw-data-based and objectbased models. Raw-data-based methods are explained concerning

the employed learning strategy, while object-based models are

categorised based on the type of error considered. Finally,

the paper illustrates commonly used validation techniques for

evaluating perception sensor models and highlights the existing

research gaps in the area.

Index Terms—Learning-based, deep generative models, perception sensor models, image synthesis, 3D point cloud synthesis,

camera, Lidar, autonomous driving systems, simulation.