Evaluating optical performance degradation of automotive cameras under accelerated aging

Automotive cameras are subject to environmental stress, which degrades performance by reducing image sharpness. To qualify for automotive use and to ensure that the cameras maintain sharpness according to the hard requirements of end-of-line testing, cameras have to undergo standardized accelerated aging tests. These tests are performed to demonstrate reliability and functional safety over lifetime. Few studies have been published that demonstrate how aging contributes to the degradation of optical performance. This study addresses this gap by combining accelerated thermal aging with sharpness tracking to investigate degradation over time. To quantify sharpness degradation, six series-production cameras were subjected to accelerated thermal aging between Image 1 and Image 2. Each camera underwent 2000 aging cycles, equivalent to 80% of their lifetime based on the Coffin-Manson model of the LV124 standard. Sharpness was measured by calculating the Spatial Frequency Response (SFR) from images captured of a double-cross reticle projected by a virtual object generator with three illumination wavelengths (625Image 3, 520Image 4, and 470Image 5). The change in sharpness was evaluated with SFR50 and SFR at 60 line pairs per millimeter (SFR@60). During the first 250 cycles, a wear-in effect was observed, where sharpness increased before leveling off, as seen previously. The results also indicated a slow decline in sharpness showing long-term stability. Analysis indicated that before aging, the best focal plane was located closer to the focal position of the red wavelength, which lies furthest from the objective. By the end of the aging process, the best focal plane had shifted toward the focal position of the blue wavelength, which is located closer to the objective. This suggests a forward movement of the image sensor due to aging. Even after 2000 cycles, all cameras maintained an SFR@60 above 0.5. A Random Forest regression model was trained to predict the age based on the SFR curves, achieving a mean absolute error of 126 cycles and a R2 score of 0.96.