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 , , and . 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.
Highlights
Advanced driver assistance system and automated driving (ADAS/AD) functions will provide many benefits such as improved passenger safety and comfort, reduced congestion and emissions and so forth
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
To be able to collect relevant reflectance measurements of materials and objects that cannot be brought into a laboratory, we introduce a new portable measurement device for collecting angle dependent reflectance values Rλ in % relative to Lambertian targets at a wavelength of λ = 945 nm, which is very close to the wavelength of most lidar types
Summary
Advanced driver assistance system and automated driving (ADAS/AD) functions will provide many benefits such as improved passenger safety and comfort, reduced congestion and emissions and so forth. Muckenhuber et al [14] introduce an object based sensor model including coverage based on a simple ray tracing approach, object dependent field of views and false negative/false positive detections based on probabilistic relationships Complex lidar models such as DIRSIG provide very detailed lidar raw data including atmospheric and thermodynamic components (http://www.dirsig.org), but are computationally demanding and not implemented into virtual test environments for testing ADAS/AD functions. Current automotive lidar models typically provide a reduced ground-truth object list or perfect point cloud without detailed information on considered material properties and corresponding lidar capabilities.
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