Polarization-coded material classification in automotive LIDAR aiming at safer autonomous driving implementations
Eduardo J. Nunes-Pereira, Hugo Peixoto, J. Teixeira, Joaquim Santos
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.