End-to-End Imaging System Optimization for Computer Vision in Driving Automation.

Full driving automation imposes to date unmet performance requirements on camera and computer vision systems, in order to replace the visual system of a human driver in any conditions. So far, the individual components of an automotive camera hav mostly been optimized independently, or without taking into account the effect on the computer vision applications. We propose an end-to-end optimization of the imaging system in software, from generation of radiometric input data over physically based camera component models to the output of a computer vision system. Specifically, we present an optimization framework which extends the ISETCam and ISET3d toolboxes to create synthetic spectral data of high dynamic range, and which models a stateof-the-art automotive camera in more detail. It includes a stateof-the-art object detection system as benchmark application. We highlight in which way the framework approximates the physical image formation process. As a result, we provide guidelines for optimization experiments involving modification of the model parameters, and show how these apply to a first experiment on high dynamic range imaging.