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• W3002881728 abstract "LIDAR sensors are one of the key enabling technologies for the wide acceptance of autonomous driving implementations. Target identification is a requisite in image processing, informing decision making in complex scenarios. The polarization from the backscattered signal provides an unambiguous signature for common metallic car paints and can serve as one-point measurement for target classification. This provides additional redundant information for sensor fusion and greatly alleviates hardware requirements for intensive morphological image processing. Industry decision makers should consider polarization-coded LIDAR implementations. Governmental policy makers should consider maximizing the potential for polarization-coded material classification by enforcing appropriate regulatory legislation. Both initiatives will contribute to faster (safer, cheaper, and more widely available) advanced driver-assistance systems and autonomous functions. Polarization-coded material classification in automotive applications stems from the characteristic signature of the source of LIDAR backscattering: specular components preserve the degree of polarization while diffuse contributions are predominantly depolarizing." @default.
• W3002881728 created "2020-01-30" @default.
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• W3002881728 date "2020-03-04" @default.
• W3002881728 modified "2023-10-16" @default.
• W3002881728 title "Polarization-coded material classification in automotive LIDAR aiming at safer autonomous driving implementations" @default.
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• W3002881728 doi "https://doi.org/10.1364/ao.375704" @default.
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