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.
© 2020 Optical Society of America
Full Article | PDF ArticleMore Like This
Yue Dong, Samuel Lawman, Yalin Zheng, Dominic Williams, Jinke Zhang, and Yao-Chun Shen
Appl. Opt. 55(13) 3695-3700 (2016)
Brian L. Collister, Richard C. Zimmerman, Victoria J. Hill, Charles I. Sukenik, and William M. Balch
Appl. Opt. 59(15) 4650-4662 (2020)
Gilles Roy, Xiaoying Cao, Robert Bernier, and Grégoire Tremblay
Appl. Opt. 59(25) 7660-7669 (2020)