Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities

Abstract

Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach that takes material properties and corresponding lidar capabilities into account. The considered material property is the incidence angle dependent reflectance of the illuminated material in the infrared spectrum and the considered lidar property its capability to detect a material with a certain reflectance up to a certain range. A new material classification for lidar modelling in the automotive context is suggested, distinguishing between 7 material classes and 23 subclasses. To measure angle dependent reflectance in the infrared spectrum, a new measurement device based on a time of flight camera is introduced and calibrated using Lambertian targets with defined reflectance values at 
 
 
 
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 . Reflectance measurements of 9 material subclasses are presented and 488 spectra from the NASA ECOSTRESS library are considered to evaluate the new measurement device. The parametrisation of the lidar capabilities is illustrated by presenting a lidar measurement campaign with a new Infineon lidar prototype and relevant data from 12 common lidar types.

Effects of Sensor Cover Damages on Point Clouds of Automotive Lidar

“…The parameters of the plane equation ax + by + cz + d = 0 . The Lambertian targets of (d), (e), and (f) were adapted from [16] where Lambertian targets from Sphere Optics [20] were used. The reflectance was calibrated with Lambertian targets.…”

“…3. Furthermore, the figure illustrates Lambertian targets with 10%, 50%, and 95% reflectance as dotted graphs [16] and two white diffuse reflective foils as solid graphs. Damages, i.e.…”

“…Following products were used: (a) Ingemann Components I-Reflect 0.23 mm [17], (b) 3M TM Scotchcal TM 3635-100 Light Enhancement Film White [18], and (c) 3M TM Engineer Grade TM DG 3 Prismatic Reflective Sheeting 3430 White [19]. The Lambertian targets of (d), (e), and (f) were adapted from[16] where Lambertian targets from Sphere Optics[20] were used. The reflectance was calibrated with Lambertian targets.…”

Remote Reflectivity Sensor for Industrial Applications

“…Contactless optical measurements are gaining significant attention for feature extraction of an object, and also for the growing needs of various industrial applications, such as computer-numerical control (CNC) machines [ 1 , 2 ], robot control [ 3 ], automated driving applications [ 4 ], and tracking systems [ 5 , 6 ]. Considering in-line measurement, non-invasive instruments are dominant due to their advantages over invasive techniques, including flexible design, reduced installation complexity, and relatively short measurement times without the risk of damage specifically in surface analysis [ 7 ].…”

Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

“…For accuracy with a specific focus, mathematical representations and models for imaging and virtualization are created for this kind of sensory system. The authors in [ 23 ] presented a new approach to LiDAR (light detection and ranging) modeling in a virtual test environment, where the sensor was replaced by a sensor model for the development and validation of reliable environmental perception systems for automated driving functions. To determine the properties of materials, the authors in [ 24 ] used a shear stress sensor system consisting of distributed embedded piezoelectric polymer films.…”