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 physics-based 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-of-the-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 object-based 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.

翻译：感知传感器，尤其是摄像头和激光雷达，是自动驾驶系统（ADS）的关键组成部分，使其能够理解周围环境以做出明智的驾驶和控制决策。因此，开发逼真的摄像头与激光雷达仿真方法（也称为摄像头和激光雷达模型）对于有效开展基于仿真的ADS测试至关重要。此外，基于深度学习的感知模型的兴起推动了感知传感器模型作为合成多样化训练数据集的重要工具的普及。传统的传感器仿真方法依赖于计算成本高昂的基于物理的算法，尤其是在ADS等复杂系统中。因此，当前潜力在于基于学习的方法，这得益于深度生成模型在合成高维数据方面的成功。本文综述了当前基于学习的传感器仿真方法和验证技术的最新进展，重点关注两类主要感知传感器：摄像头和激光雷达。本综述涵盖两类基于学习的方法，即基于原始数据的方法和基于对象的方法。基于原始数据的方法根据所采用的学习策略进行阐述，而基于对象的方法则根据所考虑的误差类型进行分类。最后，本文阐述了评估感知传感器模型常用的验证技术，并指出了该领域现有的研究空白。