This paper explores the use of existing methods found in image science literature to perform 'first-pass' specification and modeling of imaging systems intended for use in autonomous vehicles. The use of the Johnson Criteria  and suggestions for its adaptation to modern systems comprising neural nets or other machine vision techniques is discussed to enable initial selection of field of view, pixel size and sensor format. More sophisticated Modulation Transfer Function (MTF) modeling is detailed to estimate the frequency response of the system, including lower bounds due to phase effects between the sampling grid and scene . A signal model is then presented accounting for illumination spectra, geometry and light level, scene reflectance, lens geometry and transmission, and sensor quantum efficiency to yield electrons per lux second per pixel in the plane of the sensor. A basic noise model is outlined and an information theory based approach to camera ranking presented. Thoughts on progressing the above to look at color differences between objects are mentioned. The results from the models are used in examples to demonstrate preliminary ranking of differently specified systems in various imaging conditions.
Robin Jenkin, Paul Kane, "Fundamental Imaging System Analysis for Autonomous Vehicles" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines, 2018, pp 105-1 - 105-10, https://doi.org/10.2352/ISSN.2470-1173.2018.17.AVM-105