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 10 % , 50 % , and 95 % . 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.
Reducing heterogeneity in harvest material would be beneficial for wine quality and this goal may be achieved through advanced berry sorting systems. The general aim was to assess if a relationship could be found between sugar concentration and hyperspectral images. Grapes were picked at different stages of maturity and the berries were sorted according to their size and density. Hyperspectral images of the berry subsamples were obtained in the vis/NIR wavelength range with a complete spectrum from 400 nm to 2500 nm. Our results showed that vis/NIR can be used to improve the segregation of berries from all tested grape varieties based on their sugar concentration. All berries from all 12 grape varieties were used to train the regression model and the predictive power was tested on all each grape variety separately, while later validated on each variety separately, proving the possibility of using a general regression model with constant parameters to predict sugar concentration. Finally, the impact on quality was tested for red wines. Pinot noir berries with higher sugar concentrations presented more color since anthocyanin concentration was higher. Nevertheless, tannin concentration in skins and seeds tended to decrease. Berries with higher sugar concentration resulted in wines with higher anthocyanin and lower tannin concentration
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