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

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Nunes Pereira, EJ (Author) Other; Peixoto, H (Author) Other; Teixeira, J (Author) Other; Santos, J (Author) Other

Journal

Title: Applied Optics

Vol. 59

Pages: 2530-2540

ISSN: 1559-128X

Publisher: Optical Society of America

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: 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

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11