  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.
