# Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions
## 1 Introduction
However, when two manufacturers' sensors were compared, Ibeo LiDARs were able to detect targets at a lower visibility than Velodyne sensors. Only a small difference was found between advection and radiation fog's influence, so evaluation was continued using only advection fog (large droplets). Furthermore, [PERSON] et al. further evaluated the performance of their setup by using calibrated Zenith Polymer diffuse-reflective targets with reflectivities of 5%, 50%, and 90%. They were installed on a pole that a person held away from oneself. With this setup, they obtained the maximum viewing distance for each sensor. Again, these measurements even further verify that the fog decreases the maximum viewing distance drastically. Using multiple echoes and adaptive noise levels, the performance is improved but still not to a level that is sufficient for autonomous driving. In their study, [PERSON] et al. [11] evaluated and compared LiDARs with two different wavelengths of 950 nm and 1550 nm in the CEREMA fog chamber. Operating wavelength of 1550 nm is justified by its potential benefits: Less scattering in fog and more optical energy can be used because of the more relaxed eye safety regulations. However, in their study, the option for more optical power was ignored and they concentrated on measuring the scattering in fog. To analyze the visibility, they selected targets that represented typical road traffic scenarios: Mannequins as pedestrians, a traffic sign, and a car. Comparison to find out the effect of wavelength was done so that a reference visibility, from which the reference energy was measured, was chosen and reflected energy values at other visibilities were compared to the reference energy. Despite taking into account all the possible differences between the 905 nm and 1550 nm LiDARs, they were not able to find significant differences in the reflected energies. [PERSON] et al. studied the effects of real rain on LiDAR measurements outdoors [12]. They set up Velodyne VLP-16 outside to measure six areas with different materials and surfaces. Rain attribute values were obtained from a nearby meteorological station and they were assumed constant in the area under examination. Performance was evaluated by comparing the range and intensity measurements to a reference situation, i.e., no rain. Their results show variations in range measurements and decreases in returned intensity values as the rain density increases. Even though the effects of adverse weather are intensely investigated, the studies have concentrated on rain and fog, leaving out other potentially critical weather conditions. For example, the effects of snowfall and powder snow are still unknown, although estimations based on existing knowledge can be made. Still, no artificial snow simulators exist nor are there outdoor tests performed in snowy conditions. To offset this lack of knowledge, we performed tests with various LiDARs inside a fog chamber and outside in snowy conditions. These tests and their results are presented in the next two chapters. The aim is to keep the testing process practical to meet the major obstacles in roadmap towards automated driving.