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

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.

Classification of material and surface roughness using polarimetric multispectral LiDAR

“…This relation was already used for classifying highly specular car paints and certain diffuse man-made and natural targets. 17 Our previous work on a novel polarimetric multispectral LiDAR also showed the relationship between DoLP and material specularity. 18 The physical basis for the polarimetric approaches was described and discussed by Wolff and Boult.…”

Classification of material and surface roughness using polarimetric multispectral LiDAR

“…This relation was already used for classifying highly specular car paints and certain diffuse man-made and natural targets. 25 Our previous work on a novel polarimetric multispectral LiDAR also showed the relationship between DoLP and material specularity. 26 The physical basis for the polarimetric approaches was described and discussed by Wolff and Boult.…”

“…[28][29][30][31][32][33] A few attempts of polarimetric LiDAR focus on enhancing the detection of solid targets, 34,35 such as vegetation, [36][37][38][39] distant constructions, 40 and objects relevant to autonomous driving in low-visibility conditions. 25,41 However, established polarimetric LiDARs are only single-, dual-, or triple-wavelength, being thus largely limited in extracting surrounding material information accurately. Although passive and active spectral polarimeters 42,43 can tackle these challenges, a comprehensive 3D model with rich material information requires additional co-registration between the spectral polarimetric data and 3D geometry data acquired independently.…”

A new adaptive window‐based guided filtering and interpolation for polarization image demosaicing

“…The solution is to average all different O (i, j) [44]. Mathematically, the filtering output is computed by (11) and (12).…”

“…Therefore, polarization imaging technology has a strong ability to detect targets in complex backgrounds and low visibility conditions [1][2][3]. Owing to these benefits, polarization imaging has been widely used in target detection [4,5], turbid media imaging [6,7], 3D reconstruction [8], biomedical image analysis [9][10][11] and material classification [12][13][14].…”