Polarization-coded material classification in automotive LIDAR aiming at safer autonomous driving implementations

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Nunes Pereira, EJ (Autor) Outra; Peixoto, H (Autor) Outra; Teixeira, J (Autor) Outra; Santos, J (Autor) Outra

Revista

Título: Applied Optics

Vol. 59

Páginas: 2530-2540

ISSN: 1559-128X

Editora: Optical Society of America

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00R-VDR

DOI: 10.1364/ao.375704

Abstract (EN): 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. (C) 2020 Optical Society of America

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11