Windscreen Optical Quality for AI Algorithms: Refractive Power and MTF not Sufficient
This addresses a critical issue for automotive manufacturers and suppliers in ensuring reliable camera-based perception for ADAS and autonomous driving, though it appears incremental as it builds on existing industry practices.
The paper tackles the problem that existing optical quality metrics like refractive power and MTF are insufficient for characterizing windscreens in automotive systems, as they fail to capture properties relevant to AI algorithm performance, and proposes a new simulation-based concept to link optical quality to AI performance.
Windscreen optical quality is an important aspect of any advanced driver assistance system, and also for future autonomous driving, as today at least some cameras of the sensor suite are situated behind the windscreen. Automotive mass production processes require measurement systems that characterize the optical quality of the windscreens in a meaningful way, which for modern perception stacks implies meaningful for artificial intelligence (AI) algorithms. The measured optical quality needs to be linked to the performance of these algorithms, such that performance limits - and thus production tolerance limits - can be defined. In this article we demonstrate that the main metric established in the industry - refractive power - is fundamentally not capable of capturing relevant optical properties of windscreens. Further, as the industry is moving towards the modulation transfer function (MTF) as an alternative, we mathematically show that this metric cannot be used on windscreens alone, but that the windscreen forms a novel optical system together with the optics of the camera system. Hence, the required goal of a qualification system that is installed at the windscreen supplier and independently measures the optical quality cannot be achieved using MTF. We propose a novel concept to determine the optical quality of windscreens and to use simulation to link this optical quality to the performance of AI algorithms, which can hopefully lead to novel inspection systems.