# Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions
## 1 Introduction  
The light detection and ranging (LiDAR) sensors are one of the most promising options for automated driving. They provide range and intensity information from their surroundings. From the obtained data, objects can be not only detected, but also recognized (e.g., pedestrians, other vehicles). LiDAR development has been intense in recent years and, for example, their resolution has improved significantly. However, they still have their weaknesses, especially in adverse weather conditions. In order to tackle the automated driving challenges--(1) Objects on the road,(2) traffic jam ahead, (3) pedestrians crossing the road,(4) enhanced environmental awareness, and (5) drivable area awareness in all condition--the performance of LiDARs and their limits need to be investigated, and possibly, solutions need to be found for these restrictions. Primarily, two kinds of methods have been used to investigate and validate the performance of automotive LiDARs: Mathematical models and simulations, and indoor tests in fog chambers. In both methods, it is easy to reproduce and control the test environment and conditions. Additionally, there is no need to use real vehicles. This saves fuel and working hours, and is safer, especially when considering some risky scenarios in traffic. In [1], [PERSON] et al. investigated physical principles behind the influence of weather on LiDARs. Based on this, they developed a novel electro-optical laser radar target simulator system. Its idea is to reproduce the optical return signals measured by reference laser radar under adverse weather conditions. It replicates their pulse shape, wavelength, and power levels. Real-world measurements in fog, rain, and snow were performed to verify their models, though this proved to be somewhat challenging as the conditions in real traffic are neither predictable nor stable. [PERSON] et al. [2] presented a model to describe the impact of rain. The distance between object and sensor is divided into layers whose effects (absorption, transmission, and reflection of light) per layer are considered in summary. To validate their theoretical model, they built a rain simulator that was furthermore validated by comparing it to statistical data of real rain. [PERSON] et al. investigated the influence of rain on LiDARs with a small aperture [3]. They developed two models to examine the effect of two potentially performance degrading scenarios: Discrete rain drops in the proximity of a laser aperture and a protective window getting wet. In conclusion, neither of the scenarios have a significant effect on LiDARs' performance. In [4], [PERSON] et al. presented a hardware-in-the-loop testing method with real automotive cameras, which were connected to a simulated environment. In this environment, they were able to create different traffic scenarios and vary the environmental conditions, including dense fog. The method was eventually used to validate an ADAS platform available on the market. The obtained results matched the specification of the ADAS platform, showing the applicability of the developed testing method. [PERSON] et al. developed a mathematical model for the performance degradation of LiDAR as a function of rain-rate [5]. Furthermore, the model was incorporated into a 3D autonomous vehicle simulator which also has a detailed physics-based LiDAR simulation, also known as the Mississippi State University autonomous vehicle simulator (MAVS). According to their tests run in simulations of the obstacle-detection system for roof-mounted LiDAR, heavy rain does not seem to decrease the system's performance significantly. Indoor fog chambers are used both for experimentally investigating the performance of sensors [6; 7; 8; 9; 10; 11] and for validating the developed models against real conditions [1; 3]. Many have built their own chamber to mimic fog or rain and some have used the state-of-the-art fog chamber of CEREMA. Depending on the chosen chamber, the methods to modify the parameters of weather conditions inside the chamber (e.g., visibility) vary.