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    <title language="eng">Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric</title>
    <abstract language="eng">Sharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of an automotive camera, which could be the first step toward state monitoring of automotive cameras. To achieve this, Principal Component Analysis (PCA) was performed, using synthetic kernels generated by Zemax. The PCA model was built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images were created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data were utilized for algorithm development, and later on, validation was performed on real-life data. The algorithm extracts two pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel was used to blur the reference ROIs, and the resulting SFR was compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel was evaluated against the true kernel for accuracy. The structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.99, and mean squared error (MSE) from to . Validation on real-life camera images showed that the SSIM between the estimated and blurred ROI was &gt;0.82, showing promising accuracy in kernel estimation, which could be used towards in-field monitoring of camera sharpness degradation.</abstract>
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      <first_name>Amit</first_name>
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    <title language="eng">Evaluating optical performance degradation of automotive cameras under accelerated aging</title>
    <abstract language="eng">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 −40◦𝐶 and +85◦𝐶. 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 (625nm, 520nm, and 470nm). 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.</abstract>
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    <title language="eng">Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric</title>
    <abstract language="eng">The sharpness is a critical optical property of automotive cameras, measured by the Spatial Frequency Response (SFR) within the end of line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of automotive camera for state monitoring. To achieve this, Principal Component Analysis (PCA) is performed, using synthetic kernels generated by Zemax. The PCA model is built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images are created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data are utilized for algorithm development, and later on validation is performed on real-life data. The algorithm extracts two 45 x 45 pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel blurs the reference ROIs, and the resulting Spatial Frequency Response (SFR) is compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel is evaluated against the true kernel for accuracy. Structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.98, and mean squared error (MSE) from 1.1 x 10-5 to 8.3 x 10-5. Validation on real-life camera images shows that the SSIM between estimated ROI is 0.82 indicating a sufficient level of accuracy in kernel estimation to detect potential degradation of the camera.</abstract>
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    <title language="eng">Realization, multi-field coupled simulation and characterization of a thermo-mechanically robust LiDAR front end on a copper coated glass substrate</title>
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      <first_name>Marcel</first_name>
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      <first_name>Ralph</first_name>
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    <subject>
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      <value>Thermo-mechanical Simulation</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Through Focus</value>
    </subject>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
  </doc>
</export-example>
